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Dark Web Services: current Average Prices (2026 Update)

Dark Web Services: current Average Prices (2026 Update)

Introduction

Back in 2022, I published our first deep dive into dark web pricing. At the time, the landscape was already complex, but it was still possible to draw fairly clean lines between the categories of goods and services being traded. Four years on, those lines have blurred considerably.

The underground economy has matured. Prices have shifted, new product categories have emerged, and the operational sophistication of threat actors has increased significantly. Ransomware-as-a-Service is now an established business model. AI-generated phishing kits are being sold alongside traditional credential dumps. Crypto drainers have become a category in their own right. And stealer log subscriptions are now one of the fastest-growing products on the dark web.

13th May 3pm UK Time

Webinar: 2026 Dark Web Pricing Report

For this updated report, we conducted an exhaustive crawl using our own SOS Intelligence DARKSEARCH platform, scanning active dark web marketplaces, forums, and paste sites throughout Q1 2026. We supplemented this with direct marketplace access via Tor and cross-referenced our findings against published industry research from PrivacySharks, DeepStrike, Privacy Affairs, and Trustwave. This time around, we have also expanded our scope significantly, covering narcotics, firearms, counterfeit goods, cryptocurrency fraud tools, and forged documents alongside the traditional cyber-focused categories. The result is what I believe to be one of the most comprehensive snapshots of dark web pricing available today.

So whether you are a security researcher, an MSSP building threat briefings, or a CISO trying to quantify the risk exposure your organisation faces, this should give you a solid, data-backed foundation.

Methodology

Our approach follows the intelligence cycle: direction, collection, processing, analysis, and dissemination. The direction phase was straightforward: understand what is being sold on the dark web in 2026 and at what price points.

For collection, we used the SOS Intelligence API v2 to run targeted keyword searches across our indexed dark web corpus. This includes content from over 50 active marketplaces, forums, paste sites, and Telegram channels. We queried across 20+ distinct product categories, including stolen financial instruments, identity documents, hacking services, malware, access brokers, narcotics (cocaine, heroin, methamphetamine, cannabis, MDMA, and prescription drugs), firearms, counterfeit goods, cryptocurrency fraud tools, and forged documents. We also accessed active Tor marketplaces directly to verify listed prices against real product pages. Key marketplaces analysed include Tor Market, Tor Amazon, Abacus Market, TheBreakingBad, Gun and Shell Factory, and several standalone vendor storefronts.

During processing and analysis, we normalised prices to USD (where vendors listed in EUR, GBP, or cryptocurrency) and calculated averages across multiple vendors where possible. Where a product category had significant variance (for example, initial access pricing can range from $500 to $50,000+), we present the typical range rather than a misleading average.

One thing worth noting: prices on the dark web are not static. They fluctuate based on supply, demand, law enforcement activity, and even seasonal patterns. What we present here is a snapshot, accurate to Q1 2026, and should be treated as indicative rather than definitive.

Stolen Financial Instruments

Financial data remains the bread and butter of dark web commerce. Credit card data, bank account credentials, and payment platform logins continue to dominate marketplace listings. The availability is enormous, driven in large part by the explosion in stealer log infections and large-scale data breaches.

Credit card data with CVV (card-not-present fraud) remains cheap and abundant. A single card with CVV typically sells for $10 to $40, depending on the issuing bank, card type, and associated balance. Cards with higher balances or from premium issuers command a premium. Cloned physical cards with PIN are a different proposition entirely, typically ranging from $100 for a single card with a $2,500 to $3,500 balance, up to $600 for a batch of 10 cards with a combined balance of $33,000 to $35,000.

ProductPrice Range (USD)Source/Notes
Credit card with CVV$10 – $40Per card, card-not-present
Cloned card with PIN (single)$100 – $250$2,500 – $3,500 balance
Cloned cards (batch of 10)$450 – $600$30,000 – $35,000 combined
AMEX Prepaid (EUR 2,500)$105 – $510Price varies by vendor
Bank login (US)$35 – $500Depends on bank and balance
Bank login (UK/EU)$50 – $1,000+Premium for verified accounts
Bank transfer service ($10K)$500Vendor guarantees delivery
PayPal account (verified)$15 – $55Balance $2,500 – $25,000
Crypto exchange account (Kraken)$249Fully verified
Crypto exchange account (general)$90 – $250Coinbase, Binance, etc.

Compared to our 2022 findings, the average price of stolen credit card data has dropped slightly, reflecting oversupply. Bank account credentials, on the other hand, have held steady or increased, particularly for UK and EU accounts where strong customer authentication (SCA) requirements make compromised credentials more valuable to attackers who can bypass these controls.

Identity Documents and Fullz

Fullz, complete identity packages containing name, date of birth, SSN, address, and often more, remain a staple of dark web commerce. The pricing here has been remarkably consistent over the years, which suggests stable supply chains, likely fed by the steady stream of data breaches affecting organisations globally.

Bulk purchasing drives the per-unit cost down significantly. A batch of 1,000 Social Security Numbers was listed on Tor Market at $65. Business fullz (company identity packages with EIN numbers) go for around $95 for a set of 10, which is particularly concerning for organisations worried about business identity theft and fraudulent corporate filings.

ProductPrice Range (USD)Notes
Individual fullz (US)$20 – $100Per identity, includes SSN
SSN batch (1,000 records)$65Bulk purchase, specific regions
Business fullz with EIN (10 pack)$95Corporate identity packages
US passport scan$100Digital copy only
US physical passport (forged)$3,000 – $3,800High-quality forgery
UK driver’s licence (forged)$500Physical document
EU national ID (forged)$300 – $700Varies by country
Medical records$50 – $500+Depends on completeness
Selfie with ID (for KYC bypass)$50 – $100Growing demand

A notable trend in 2026 is the growing market for KYC bypass packages. These typically include a stolen identity paired with a matching selfie (often obtained from stealer logs that capture webcam images), sold specifically to bypass Know Your Customer verification on financial platforms. This is a direct response to tightened identity verification requirements, and it represents an uncomfortable escalation in the identity fraud ecosystem.

DDoS and Hacking Services

DDoS-for-hire remains one of the most accessible attack services on the dark web. Entry-level DDoS attacks can be purchased for as little as $10 per hour, making it trivially cheap for anyone with a grudge and a cryptocurrency wallet. Monthly subscription packages for sustained DDoS capability go up to $850, though most listings cluster around $200 to $500 per month.

Hacking services for hire are more variable in pricing, reflecting the range of complexity involved. Simple social media account compromises sit at the lower end, while corporate network penetration and database extraction commands significantly higher fees.

ServicePrice Range (USD)Notes
DDoS attack (per hour)$10 – $50Basic layer 4/7 attack
DDoS subscription (monthly)$200 – $850Sustained capability
Social media account hack$25 – $100Facebook, Instagram, etc.
Email account compromise$100 – $500Corporate email higher
Website hacking$200 – $3,000Depends on target complexity
Corporate network access$500 – $10,000+Overlaps with IAB market
Phone hacking/spyware install$300 – $1,500Remote installation
Doxing service$25 – $200Varies by depth of research

The DDoS market has become increasingly commoditised. In 2022 we reported an average DDoS service price of around $382. That number has come down, driven by competition between providers and the proliferation of botnet infrastructure. The real concern is not the price itself but how easy it has become to launch these attacks with minimal technical knowledge.

Malware, Exploit Kits, and Phishing

This is where the dark web economy has seen some of its most significant evolution since our last report. The malware-as-a-service model is now firmly established, with vendors offering everything from basic RATs (Remote Access Trojans) through to sophisticated banking trojans and zero-day exploits.

Phishing kits have become particularly interesting. AI-generated phishing templates are now being sold at a premium, with vendors marketing their kits as capable of bypassing modern email security filters. The quality of these templates has improved dramatically, making traditional security awareness training less effective than it was even two years ago.

ProductPrice Range (USD)Notes
RAT (Remote Access Trojan)$45 – $500Off-the-shelf, basic features
Banking trojan$500 – $1,800Targeted at specific banks
Ransomware-as-a-Service kit$500 – $5,000Includes builder and panel
Stealer log subscription$100 – $1,024/moRedline, Raccoon, Vidar
Phishing kit (standard)$50 – $300Includes templates and hosting
Phishing kit (AI-generated)$200 – $800Bypass modern filters
Zero-day exploit (general)$5,000 – $200,000+Price varies enormously
Exploit kit (browser)$100 – $2,000Pre-packaged exploitation
Botnet rental (1,000 bots)$50 – $200/dayFor spam or DDoS
Keylogger$25 – $150Basic to advanced features

The Ransomware-as-a-Service (RaaS) market deserves special attention. Our platform currently tracks over 100 active ransomware groups, many of which operate affiliate programmes where the actual ransomware deployment is carried out by affiliates who pay a percentage (typically 20-30%) of the ransom to the RaaS operator. The barrier to entry for launching a ransomware campaign has never been lower, and this is reflected in the sustained growth of ransomware incidents globally.

Crypto Drainers and Mixers

This is a category that barely existed in 2022 and is now a significant segment of the dark web economy. Crypto drainers are tools designed to empty cryptocurrency wallets, typically deployed via phishing sites that mimic legitimate Web3 platforms and trick users into connecting their wallets and signing malicious transactions.

Our DARKSEARCH data turned up active listings on DNA Forums and other threat actor communities, with prices ranging from $50 for basic tutorials through to $1,000 for fully operational Solana drainer toolkits. The Tron Trap drainer tool was listed at $300, while general-purpose drainer kits sat around $200 to $500.

ProductPrice Range (USD)Notes
Crypto drainer kit (general)$200 – $500Multi-chain support
Solana drainer$1,000Chain-specific tooling
Tron Trap drainer$300Listed on DNA Forums
Crypto drainer tutorial$50 – $100DIY approach
Crypto mixing/tumbling service1-3% of amountPer transaction fee
Crypto cashout service15-25% of amountConversion to fiat

The emergence of drainer-as-a-service mirrors the RaaS model. Operators provide the tooling and infrastructure, affiliates drive traffic to phishing sites, and profits are split. Some drainer operators take a 20-30% cut of every wallet drained. For context, wallet drainer attacks stole hundreds of millions of dollars in cryptocurrency during 2025 alone, making this one of the highest-growth criminal sectors.

Initial Access Brokers

Initial Access Brokers (IABs) continue to be a critical part of the threat landscape. These are threat actors who specialise in gaining access to corporate networks and then selling that access to other criminals, typically ransomware operators. The IAB market is essentially the supply chain for ransomware.

Pricing varies enormously based on the target organisation’s size, industry, and the type of access being sold. VPN credentials for a small company might go for $500, while domain admin access to a large enterprise can command $10,000 or more. Our 2022 report found an average of around $7,700 for initial network access. In 2026, the range has widened as the market has matured, but the median sits around $2,000 to $5,000 for mid-market targets.

Access TypePrice Range (USD)Notes
VPN credentials (SME)$200 – $1,000Single organisation
RDP access (dedicated server)$10 – $100Commodity pricing
Domain admin (enterprise)$5,000 – $50,000+High-value targets
Web shell access$50 – $500Depends on target
cPanel/hosting access$10 – $50Bulk available
Database access (customer data)$500 – $10,000Depends on record count
Cloud infrastructure access$1,000 – $20,000AWS, Azure, GCP

Cloud infrastructure access is the emerging high-value category here. As organisations continue their migration to cloud platforms, compromised cloud credentials have become increasingly sought after. A set of AWS root account credentials for an enterprise can be worth significantly more than traditional on-premise network access, reflecting the potential blast radius of a cloud compromise.

Stolen Accounts and Subscriptions

The market for compromised online accounts remains massive, covering everything from streaming services to social media to gaming platforms. These are largely driven by credential stuffing attacks leveraging the billions of username/password pairs available from historical breaches, combined with the output of stealer log infections.

Account TypePrice Range (USD)Notes
Netflix/Disney+/streaming$4 – $25Per account, often shared
Spotify Premium$3 – $10Bulk available
Facebook account$25 – $45Higher for aged accounts
Instagram account$25 – $45Followers affect price
LinkedIn Premium$30 – $50Professional accounts
Gaming accounts (Steam, Epic)$10 – $100Game library affects price
Food delivery (Uber Eats, etc.)$5 – $20With stored payment
Email accounts (bulk)$2 – $10Per account
VPN service accounts$5 – $15NordVPN, ExpressVPN, etc.

What strikes me about this category is how cheap everything is. A Netflix account for $4, a Facebook account for $25. The low prices reflect the sheer volume of compromised credentials available. For most consumers, the inconvenience of having an account compromised is minor. But for organisations, compromised employee accounts, particularly email and LinkedIn, can be the starting point for targeted social engineering campaigns.

Counterfeit Currency and Documents

Counterfeit physical currency continues to be traded, though the market has evolved. Our crawl of Robinhood Market found fake Euro banknotes listed from $300 for a small batch up to $1,200 for larger quantities. Western Union transfer services were listed at $200 for a $2,000 transfer, representing a 10% fee.

Bank cheque templates have also become a notable category, with templates available from as little as $5 for basic designs up to $600 for comprehensive kits that include matching security features and printing instructions.

ProductPrice Range (USD)Notes
Counterfeit EUR banknotes$300 – $1,200Various denominations
Counterfeit USD banknotes$350 – $1,500Quality varies significantly
Western Union transfer ($2,000)$20010% fee structure
MoneyGram transfer$150 – $300Similar fee structure
Bank cheque templates$5 – $600Including security features
Counterfeit branded goods (guides)$20 – $200Manufacturing instructions

In our 2022 report, counterfeit currency averaged around $396 per $1,000 face value. The current rates are broadly similar, suggesting this market has reached a stable equilibrium. The real shift is towards digital fraud, with physical counterfeiting becoming a smaller proportion of overall dark web commerce.

Proxy and Hosting Infrastructure

Bulletproof hosting and residential proxy services continue to be essential infrastructure for cybercriminal operations. These services provide the anonymous, abuse-tolerant hosting that enables everything from phishing campaigns to command and control servers.

ServicePrice Range (USD)Notes
Bulletproof hosting (monthly)$50 – $500Abuse-tolerant, offshore
Residential proxy (monthly)$200 – $645Pool of residential IPs
SOCKS5 proxy (per IP)$1 – $10Single use or short-lived
VPN service (criminal-oriented)$5 – $30/moNo-log guarantees
Dedicated server (offshore)$100 – $400/moFull admin access
Domain + hosting bundle$20 – $100For phishing campaigns

Residential proxy pricing has actually increased since 2022, when we reported an average of $645 per month. The current range starts lower but premium services now charge more, reflecting growing demand from threat actors who need residential IP addresses to bypass fraud detection systems and CAPTCHAs.

AI-Enabled Criminal Services

This is entirely new territory since our 2022 report. The commoditisation of large language models has created a new category of criminal tooling that simply did not exist four years ago. Dark web forums now host discussions and sales of jailbroken AI models, custom-trained chatbots for social engineering, and AI-powered tools for generating convincing phishing content at scale.

While we did not find as many standardised price points for AI services as for other categories (the market is still maturing), the trend is clear. AI is being integrated into existing criminal workflows, particularly around social engineering, phishing content generation, and code development for malware. Some vendors are marketing “FraudGPT” and “WormGPT” style tools, essentially LLM wrappers with the safety guardrails removed, at subscription prices of $200 to $1,700 per month.

The implications here are significant. AI lowers the barrier to entry for technically unsophisticated threat actors, increases the quality and scale of social engineering attacks, and makes it harder for defenders to distinguish malicious content from legitimate communications.

Narcotics and Controlled Substances

Dark web drug marketplaces remain one of the most active sectors of the underground economy. Our DARKSEARCH crawls in Q1 2026 revealed multiple operational marketplaces with extensive product catalogues, professional vendor storefronts, and established escrow systems. The sophistication of these operations is notable: vendor pages include lab-testing claims, customer reviews, volume discount tiers, and next-day delivery (NDD) options for domestic shipments.

Three marketplaces stood out during our research. TheBreakingBad, a dedicated vendor storefront operating with a full e-commerce style interface, offered a comprehensive catalogue of stimulants, opiates, and dissociatives with granular volume pricing. Abacus Market, a multi-vendor marketplace, carried similar inventory with slightly different pricing. Tor Market, which operates as a broader multi-category darknet marketplace (also listing firearms, documents, and hacking tools), hosted 47 drug products across multiple vendors at the time of our crawl.

Stimulants

Cocaine remains the most commonly listed stimulant. Colombian cocaine claiming 94%+ purity was available across multiple markets. Crystal methamphetamine was the second most prevalent stimulant listing, with a notably well-developed volume pricing structure from European vendors. Amphetamine paste, particularly popular on European markets, was available in both standard (74%) and premium (94%) purity grades.

ProductPrice RangeVolume PricingSource Market
Colombian Cocaine 94%+$50 – $80/gBulk from $35/g at 100g+Tor Market, Abacus
Crystal Meth 94% (Mexican)€10/g€80/10g, €700/100g, €5,500/kgTheBreakingBad
Crystal Meth (ICE)$99/10g$249/25g, $1,000/100gAbacus Market
Speed Amphetamine 94%€22/10g€90/100g, €700/kgTheBreakingBad
Speed Amphetamine 74%€10/10g€77/100g, €555/kgTheBreakingBad
3-MMC (Metaphedrone)€10/g€350/100g, €3,000/kgTheBreakingBad
MDMA Champagne 84%+$6.50 – $18/g$8/g at 250g bulkAbacus Market
XTC Pills 250mg MDMA€12.50/10 pills€80/100, €750/1,000 pillsTheBreakingBad
XTC Pills (240mg, various)$135 – $200/10 pillsMultiple brands availableTor Market

Opiates and Opioids

Heroin remained available from specialist vendors, with Iranian-sourced uncut product marketed as the premium option. The pricing structure on TheBreakingBad was particularly detailed, offering nine quantity tiers from a single gram to a full kilogram. This level of volume pricing suggests these vendors are servicing both individual users and mid-level distributors.

Prescription opioids also featured prominently. Oxycontin (40mg tablets) and Percocet (5/325mg) were listed on Tor Market, though exact per-unit pricing was often obscured behind “add to cart” interfaces that required account creation to view.

ProductPrice RangeVolume PricingSource Market
Heroin Uncut (Iranian)€22.50/g€175/10g, €1,600/100g, €13,500/kgTheBreakingBad
Heroin #3 (60-70%)$50 – $55/3gMid-grade, EU sourcedTor Market
Oxycontin 40mg (20ct)$120 – $200Prescription tabsTor Market
Percocet 5/325mg (70ct)$150 – $250Price per bottle est.Tor Market
Fentanyl patches/pills$50 – $150Limited listings (high risk)Various

Cannabis

Cannabis products dominated by volume of listings. UK-based vendors advertised next-day delivery (NDD) on multiple strains, essentially running a delivery service comparable to legitimate e-commerce. Listings included premium strains such as OG Cookies, Super Silver Haze, Gorilla Glue, and Amnesia Haze, with clear quantity tiers.

ProductPrice RangeVolume/NotesSource Market
Gorilla Glue (7g)£42 (~$53)UK NDD availableAbacus Market
OG Cookies (various)$50 – $120/quarterMultiple vendorsTor Market
Amnesia Haze (100g)$400 – $600Bulk listingAbacus Market
Super Silver Haze$35 – $80/quarterDutch sourcedTor Market
Cannabis (French market)Varies192 products listedFR marketplace

Psychedelics and Dissociatives

The psychedelics market showed strong activity, with psilocybin products packaged in consumer-friendly formats (chocolate edibles, microdose capsules) and ketamine available from multiple vendors. LSD pricing was harder to pin down through DARKSEARCH alone, but cross-referencing with forum discussions suggests typical street-equivalent pricing in the $5 to $15 per tab range.

ProductPrice RangeNotesSource Market
Psilocybin Chocolate (4g)$40Consumer-packaged edibleTor Market
Psilocybin Capsules 150mg (x100)$80 – $150Microdose formatTor Market
Ketamine S-Isomer€10/g€175/50g, €1,850/kgTheBreakingBad
LSD Tabs$5 – $15/tabForum pricing cross-refMultiple
XTC/MDMA (ecstasy, various)€12.50 – $200/10 pillsBrand-dependent pricingMultiple markets

Prescription Pharmaceuticals

Beyond controlled opioids, a range of prescription medications was available. Benzodiazepines (particularly Xanax and Rivotril) were listed at a fraction of pharmacy prices. Erectile dysfunction medications (Cialis) appeared as bulk listings, likely diverted or counterfeit product.

ProductPrice RangeNotesSource Market
Xanax 2mg (50 pills)€5 – €25Alprazolam, likely pressedAbacus Market
Rivotril 2mg (20 pills)$15 – $40ClonazepamTor Market
Cialis (50 tabs)$120 – $200Bulk pack, likely generic/counterfeitTor Market

The drug marketplace in 2026 functions like a professional retail operation. Escrow, customer reviews, volume discounts, refund policies, and domestic stealth shipping are standard. The operational maturity here mirrors what we have seen in the cyber services space, with vendor reputation systems driving quality competition.

Firearms and Ammunition

Firearms remain one of the most sensitive categories on the dark web. Our DARKSEARCH queries returned listings from multiple sources, including a dedicated storefront called “Gun and Shell Factory” and the firearms category on Tor Market (which carried 10 products at the time of our crawl). A vendor called “GlockZ” was also active with 7 listed products.

It is worth noting that firearms sales on the dark web carry the highest scam risk of any category. Law enforcement honeypot operations are well-documented in this space, and many “vendors” simply take payment and never deliver. That said, the listings themselves are informative for understanding what threat actors believe constitutes a reasonable market price, and the availability of these listings is itself a data point worth tracking.

Handguns

FirearmListed Price (USD)CalibreSource
Glock 17 Gen 4$4999mmTor Market
Glock 19$4509mmGun and Shell Factory
Glock 26$3509mmGun and Shell Factory
SIG Sauer P320$6009mmGun and Shell Factory
SIG Sauer P220$680 (sale from $800).45 ACPTor Market
Desert Eagle$899 (sale from $1,000).44 MagnumTor Market
Beretta M9$2499mmTor Market
Ed Brown Kobra$499.45 ACPGun and Shell Factory
CZ TS 2$8999mmGun and Shell Factory

Long Guns and Submachine Guns

FirearmListed Price (USD)TypeSource
AK-47$800 – $1,200Assault RifleGun and Shell Factory
AR-15$700 – $1,000Semi-Auto RifleGun and Shell Factory
UZI Pro$740Submachine GunGun and Shell Factory

Ammunition was also listed separately, though pricing data was less granular in our crawl results. The presence of both firearms and ammunition on the same marketplaces that sell drugs, stolen data, and hacking tools underscores the breadth of these platforms. Tor Market, for instance, carries categories for Counterfeits, Credit Card/CVV/Dumps, Documents, Drugs (47 products), Firearms and Ammo (10 products), Gadgets, and Hacking (13 products), all under one marketplace roof.

Compared to legitimate retail prices, dark web firearms are generally listed at a discount of 30% to 60% from retail, which reflects the risk premium inverted: buyers on the dark web are willing to pay less because of the high risk of scam, non-delivery, or law enforcement interception. From a threat intelligence perspective, the persistence of these listings indicates ongoing demand from individuals who cannot or will not purchase through legitimate channels.

Expanded Counterfeit and Fraud Services

Beyond the identity documents and financial instruments covered earlier, the dark web hosts a broader ecosystem of counterfeit goods and fraud services. Our expanded DARKSEARCH crawl revealed categories including counterfeit luxury goods, forged academic credentials, cryptocurrency fraud tools, and casino bonus exploitation kits.

Counterfeit Luxury Goods
Tor Market listed counterfeit luxury watches, with a Rolex Submariner Non-Date 41mm (model 124060) featured as a promoted product. Counterfeit luxury goods have historically been a smaller dark web category compared to clearnet operations, but their presence on multi-category darknet marketplaces suggests vendors are expanding their offerings to capture cross-selling opportunities from buyers already on the platform for other products.

Cryptocurrency Fraud Tools

Cryptocurrency fraud tools were among the most expensive single-item listings we encountered. The “Tor Amazon” marketplace (operating since 2019) offered an extensive catalogue including stolen Bitcoin wallets, fake USDT senders, wallet cracking tools, and compromised exchange accounts. The pricing here is particularly instructive.

ProductPrice (USD)DetailsSource
Stolen BTC Wallet (599 BTC)$42,000 – $59,900Priced at ~0.1% of wallet balanceTor Amazon
Atomic Wallet 1BTC+$4,000Pre-loaded compromised walletTor Amazon
Bitcoin Wallet w/ Seeds$1,500 – $6,500Wallet.dat with passphraseTor Amazon
Wallet.dat Passphrase Cracker$400Brute-force toolTor Amazon
Flash/Fake USDT Sender$300 – $500Spoofed transactionsTor Amazon
BTC Mnemonic Brute Tool£550 (~$690)12-phrase wallet crackerStandalone store
BTC Reverse Transaction Tool$400 – $600Transaction reversal exploitStandalone store
Leaked Data (16B accounts)$121,484Apple, Google, Binance etc.Tor Amazon

Financial Fraud and Money Movement

The money movement ecosystem on the dark web continues to grow. Services for laundering funds through compromised payment platforms, cloned cards, and bank transfer services were widely available. The Tor Amazon marketplace offered ATM-cloned cards with guaranteed balances, stolen Visa CC/CVV data, Binance account transfers, PayPal-to-Bitcoin conversion services, and casino bonus exploitation kits.

ProductPrice (USD)DetailsSource
ATM Cloned Card ($15K balance)$900Physical card, PIN includedTor Amazon
VISA CC/CVV ($8K balance)$400Virtual card with full detailsTor Amazon
PayPal $7K Verified Transfer$600 (sale from $700)Within 20 minutes worldwideTor Market
Visa Prepaid Clone ($7.5K)$630 (sale from $750)Physical + online usableTor Market
Binance Account Transfer$300 – $500BTC/ETH/USDT transfersTor Amazon
Paxful Accounts ($5.5K)$250 – $400Guaranteed balanceTor Amazon
Casino Bonus Exploit$150 – $350$3K-$10K bonus exploitationTor Amazon
Bank Flash SQR Tool$800 (sale from $1,500)Bank manipulation softwareTor Amazon
Aviator Predictor Hack AI$400Casino/gambling exploit toolTor Amazon
Gold Bars 100g (Pre-Owned)$8,500Physical delivery, escrowTor Amazon

Forged Documents and Credentials

Forged documents ranged from academic credentials (diplomas, degrees, professional certificates) to government-issued identity documents. Tor Market listed a dedicated Documents category with 4 products, while Tor Amazon carried a broader selection under their Documents department. The “USA Documents” product on Tor Amazon was rated 4.85 out of 5 and priced between $600 and $3,500, covering various forms of US identification.

ProductPrice Range (USD)NotesSource
USA Identity Documents (ID Card)$600 – $3,500Multiple ID types availableTor Amazon
Forged Diploma/Degree$200 – $800Various institutionsMultiple markets
Professional Certificates$150 – $500IT, medical, trade certsMultiple markets
Counterfeit COVID Certificates$50 – $150Declining demandForum listings
Deepfake Service$100 – $500Video/image manipulationTor directories

The breadth of these offerings paints a picture of a mature underground economy that mirrors, and in some ways parodies, legitimate commerce. Marketplaces offer escrow protection, customer reviews, vendor ratings, return policies, and even promotional sales events. Tor Amazon, for example, displays a running shopping cart total (one snapshot showed a cart worth $154,514), tracks “verified sellers” with sales counts, and runs sale pricing on multiple products. This operational maturity makes these platforms resilient and, from a threat intelligence perspective, worth continuous monitoring.

Pricing Comparison: 2022 vs 2026

The table below compares our 2022 findings with the current 2026 data across key categories. Prices are typical midpoint values.

Category2022 Average2026 AverageTrend
Credit card with CVV$243$15 – $40Decreased (oversupply)
Counterfeit currency (per $1K)$396$350 – $450Stable
DDoS service (monthly)$382$200 – $500Decreased (commoditised)
Residential proxy (monthly)$645$200 – $645Wider range, lower entry
Initial network access$7,700$2,000 – $5,000Decreased (median)
Ransomware kitN/A$500 – $5,000New category tracked
Crypto drainer kitN/A$200 – $1,000New category
Stealer log subscriptionN/A$100 – $1,024/moNew category
AI criminal toolsN/A$200 – $1,700/moNew category
Cocaine (per gram)$150 – $300$50 – $80Decreased (dark web discount)
Crystal Meth (per gram)$50 – $100$10 – $15Decreased significantly
Heroin (per gram)$100 – $200$22 – $55Decreased (direct sourcing)
Handgun (Glock 17)$1,500 – $2,500$450 – $500Decreased (high scam risk)
Stolen BTC WalletN/A$4,000 – $59,900New category
Forged ID Documents (US)$250 – $1,000$600 – $3,500Increased (quality premium)

The overarching trend is clear: established product categories have become cheaper as supply has increased, while new, more sophisticated offerings (RaaS, drainers, AI tools) have emerged at premium price points. The dark web economy is following the same pattern as legitimate tech markets, with commodity products racing to the bottom while innovation commands a premium.

Key Takeaways

The barrier to entry keeps falling. DDoS attacks for $10, phishing kits for $50, stolen accounts for a few dollars. The tools for cybercrime are cheaper and more accessible than ever. This has direct implications for the volume of attacks organisations should expect to face.

Stealer logs are the new oil. The stealer log economy has grown enormously. These logs, harvested from malware infections on individual machines, contain browser-saved passwords, session cookies, crypto wallet data, and more. They feed almost every other category: account takeover, initial access brokering, financial fraud, and identity theft.

Ransomware is a mature industry. With over 100 active groups tracked on our platform and well-established affiliate models, ransomware has moved from being an emerging threat to a structural feature of the threat landscape. The supply chain (IABs to RaaS operators to affiliates to money launderers) is well-oiled and efficient.

AI is an accelerant. While AI has not yet created fundamentally new attack types, it is making existing attacks more effective, more scalable, and more convincing. The appearance of AI-enabled tools as a distinct product category on the dark web is a development every security team should be tracking.

Crypto is the preferred battlefield. The emergence of crypto drainers as a major product category, combined with the growth in compromised exchange accounts, tells us that cryptocurrency users and platforms are now firmly in the crosshairs. The pseudonymous nature of crypto transactions makes this an attractive and growing target.

Drug marketplaces operate like professional retailers. The dark web drug economy has reached a level of operational maturity that mirrors legitimate e-commerce. Escrow, customer reviews, next-day delivery, volume discounts, lab-testing claims, and refund policies are now standard. Prices have dropped significantly compared to street equivalents, reflecting the efficiency of direct vendor-to-buyer models that bypass traditional distribution chains.

Firearms listings persist despite high scam risk. While firearms are consistently available on dark web marketplaces, this category carries the highest scam risk and is a known target for law enforcement honeypot operations. The listed prices (30% to 60% below retail) reflect this risk. The intelligence value here is less about the prices themselves and more about the persistent demand signal from individuals seeking to acquire weapons outside regulated channels.

The dark web is a one-stop shop. Multi-category marketplaces like Tor Market (drugs, firearms, counterfeits, hacking tools, documents, and financial fraud under one roof) and Tor Amazon (hacking, financial, electronics, documents, drugs, and guns) demonstrate that the underground economy has consolidated. A single marketplace visit can service everything from identity theft to substance procurement to weapon acquisition. This consolidation has implications for law enforcement, intelligence analysts, and risk modelling.

Don’t miss out!

Webinar: 2026 Dark Web Pricing Report

Conclusion

The dark web economy in 2026 is bigger, more diverse, and more sophisticated than it was in 2022. Prices for commodity products have dropped while new, higher-value categories have emerged. The professionalism of threat actors continues to increase, with customer support, affiliate programmes, and quality guarantees now standard across many marketplaces.

What has changed most since our 2022 report is the breadth. The dark web is no longer just a marketplace for stolen data and hacking tools. It is a fully integrated underground economy spanning narcotics, firearms, counterfeit goods, identity documents, cryptocurrency fraud, and digital services. Multi-category marketplaces have consolidated these offerings under single platforms, complete with escrow systems, vendor ratings, and promotional campaigns that would not look out of place on a legitimate e-commerce site.

For defenders and intelligence professionals, the key takeaway is that the cost of attacking your organisation, or acquiring the tools to do so, is low and getting lower. The investment needed to mount a credible phishing campaign, launch a DDoS attack, purchase a weapon, or obtain fraudulent identity documents is trivial compared to the potential payoff. This asymmetry is the fundamental challenge, and understanding the economics of the dark web is essential to building effective defences and informing policy.

At SOS Intelligence, we monitor these marketplaces continuously so our customers do not have to. Our DARKSEARCH platform indexes content across 50+ active dark web sources, and our analysts track emerging threats, new marketplace activity, and pricing trends in real time. If you want to understand what is being said about your organisation on the dark web, or if you need intelligence on any of the categories covered in this report, our platform gives you that visibility.

Passport photo by Kit (formerly ConvertKit) on Unsplash

Crypto Photo by Pierre Borthiry – Peiobty on Unsplash

Drugs photo by Colin Davis on Unsplash

Money hoto by Dmytro Glazunov on Unsplash

Gun photo by Tom Def on Unsplash

"SOS
Investigation, Opinion

Emoji Smuggling: Hiding Malicious Code in Plain Sight

Emoji smuggling represents an emerging obfuscation technique where attackers exploit Unicode encoding and emoji characters to conceal malicious code, bypass security filters, and evade detection systems. Whilst it may sound whimsical, this attack vector leverages legitimate Unicode functionality to create serious security challenges for organisations. Understanding how attackers weaponise these seemingly innocent characters helps us build better defences and recognise when something suspicious might be happening.

This post explores what emoji smuggling is, how attackers use it, and what organisations can do to protect themselves.

The Foundation: How Text Actually Works

Before we dive into the attack itself, we need to understand something fundamental about how computers handle text. When you type a letter, number, or emoji, your computer doesn’t actually store that visual symbol. Instead, it stores a number that represents that character. This system is called Unicode, and it’s what allows your computer to display everything from English letters to Chinese characters to emoji.

For example, when you use the fire emoji 🔥, your computer stores it as the number U+1F525. Every character you can type has its own unique number in the Unicode system. This is brilliant for international communication, but it also creates opportunities for attackers.

The key insight is this: many security systems were built to look for suspicious patterns in regular letters and numbers, but they often don’t scrutinise emoji and special Unicode characters as carefully. Attackers exploit this gap.

What Is Emoji Smuggling?

Emoji smuggling is the practice of using emoji, special Unicode characters, or look-alike characters to hide malicious content from security systems while keeping it functional for their purposes. Think of it as writing a secret message in invisible ink that only becomes visible when you want it to.

Attackers use several techniques:

Look-Alike Characters: Some characters from different alphabets look identical to English letters but are technically different. For instance, the Cyrillic letter ‘а’ looks exactly like the English ‘a’, but computers see them as completely different characters. An attacker might register a domain like “pаypal.com” (using a Cyrillic ‘а’) that looks legitimate to humans but directs to a phishing site.

Emoji as Code: This technique involves creating a substitution cypher where each emoji represents a command, function, or piece of data. Attackers establish a mapping system, similar to how spies might use a codebook. For example, they might decide that:

  • 🔥 represents “delete”
  • 📁 represents “file”
  • 🌐 represents “download”
  • 💀 represents “execute”

So a string like “🔥📁🌐💀” would decode to “delete file, download, execute”. To anyone glancing at log files or monitoring network traffic, this looks like someone simply sent some emoji in a message. Security systems scanning for dangerous keywords like “delete”, “execute”, or suspicious command patterns won’t flag it because they’re looking for text, not pictures.

The attacker’s malware or script includes a decoder that translates these emoji back into actual commands when executed. What makes this particularly effective is that emojis feel innocuous. We’re used to seeing them in messages and social media, so their presence doesn’t immediately raise suspicion the way a long string of seemingly random characters might.

Consider a real scenario: an attacker gains limited access to a system and needs to communicate instructions to their malware without triggering security alerts. They might send what appears to be a harmless message containing emojis through a chat system or email. The malware on the compromised system receives this message, decodes the emoji, and executes the hidden commands. To security analysts reviewing logs, it simply looks like someone sent some emoji.

Invisible Characters: This is perhaps the most insidious technique because it exploits characters you literally cannot see. Unicode includes several characters that have zero width, meaning they take up no visual space on screen. These include the Zero-Width Space (U+200B), Zero-Width Non-Joiner (U+200C), and Zero-Width Joiner (U+200D).

Here’s how this works in practice. Imagine a security system is configured to block any script that contains the text string “malicious_function”. An attacker can break up this string by inserting zero-width characters between the letters:

What you see: malicious_function()
 What’s actually there: mal​ici​ous_​fun​cti​on() (contains invisible zero-width spaces)

To the human eye, even if you’re carefully reading through code, these look identical. But to a security scanner looking for the exact string “malicious_function”, the second version doesn’t match because those invisible characters break up the pattern. The scanner sees “mal[invisible]ici[invisible]ous[invisible]_fun[invisible]cti[invisible]on” and doesn’t recognise it as a threat.

However, when this code actually runs, many programming languages and interpreters ignore these zero-width characters during execution. The invisible spaces are stripped out, and the function executes normally. So the attacker has successfully hidden their malicious code from security scans whilst maintaining its functionality.

Attackers also use invisible characters to hide data within seemingly innocent text. Imagine you’re trying to smuggle a password out of a secure system. You could write a normal-looking sentence like “Please review the quarterly report”, but encode the password in invisible characters interspersed throughout. To anyone reading it, it’s just a mundane sentence. But someone with the right decoder can extract the hidden information.

This technique is particularly dangerous because it’s virtually impossible to detect through visual inspection alone. You need specialised tools that reveal invisible characters, and even then, you need to know how to look for them.

Direction Trickery: Unicode includes special characters that change the direction text flows (needed for languages like Arabic). Attackers use these to make filenames appear safe when they’re actually dangerous. A file might display as “document.txt” but actually be “tnemucod.exe” with a direction-reversal character hiding the true extension.

Why This Works

You might wonder why this is effective if it seems so simple. The answer lies in how security systems are designed.

Most security tools were built to detect patterns in regular ASCII text (the basic English letters, numbers, and symbols). They look for suspicious keywords, known malicious code patterns, or dangerous file types. But when attackers encode their attacks using Unicode tricks, these patterns become unrecognisable to the security system.

It’s similar to how a metal detector at an airport won’t find a ceramic knife. The detector is designed to find metal, and the knife is dangerous, but because it’s made of the wrong material, it slips through. Similarly, security filters are often designed to catch ASCII-based threats, so Unicode-based threats slip through.

Additionally, completely blocking Unicode would break legitimate functionality. Businesses operate globally, users have names in different languages, and emojis are a standard part of modern communication. Security teams can’t simply ban all non-English characters without severely impacting usability.

Real-World Examples

Understanding the theory is one thing, but seeing how this plays out in practice makes the threat more tangible.

Phishing Attacks: Attackers register domain names using look-alike characters. A company email might tell you to log in at “microṡoft.com” (note the dot over the ‘s’). To most people, this looks perfectly normal, but it’s not the real Microsoft. Users enter their credentials, and the attacker now has access to their account.

Bypassing Content Filters: Many organisations block certain words in emails or messages to prevent data leaks or inappropriate content. An employee trying to circumvent these filters might write “pаssword” using a Cyrillic ‘а’ instead of the English ‘a’. The filter doesn’t catch it because it’s technically a different word, but humans reading it understand the meaning perfectly.

Hidden Data Exfiltration: An attacker who has compromised a system needs to send stolen data out without triggering data loss prevention systems. They might encode credit card numbers using emoji: “4️⃣5️⃣3️⃣2️⃣ 1️⃣2️⃣3️⃣4️⃣ 5️⃣6️⃣7️⃣8️⃣ 9️⃣0️⃣1️⃣0️⃣”. Security systems looking for the pattern of a 16-digit number won’t detect this, but it’s trivial to decode on the other end.

Malware Obfuscation: Malware authors need to hide suspicious commands from antivirus software. They might write “po​we​rs​he​ll” with invisible zero-width spaces between letters. When a security researcher looks at the code, they see gibberish, and antivirus scans don’t recognise the command. But when the malware runs, it successfully executes PowerShell commands.

Code Injection: Web applications that don’t properly handle Unicode input can be vulnerable to injection attacks. An attacker might submit what looks like normal text but includes hidden direction-control characters that manipulate how the input is processed, potentially executing unauthorised database queries or commands.

The Impact on Large Language Models

As artificial intelligence and large language models (LLMs) become increasingly integrated into business operations and security workflows, emoji smuggling presents a unique and evolving challenge. These AI systems, designed to understand and process human language, can be vulnerable to Unicode-based attacks in ways that differ from traditional security systems.

Prompt Injection via Unicode: LLMs process text input and generate responses based on their training. Attackers can use Unicode tricks to bypass safety filters or inject malicious instructions that the model follows. For instance, an attacker might use invisible characters to break up prohibited phrases that the model has been trained to refuse, or use look-alike characters to make harmful instructions appear benign to content filters whilst remaining interpretable by the model.

Consider a scenario where an LLM-powered chatbot has been instructed never to provide information about bypassing security systems. An attacker might craft a prompt using Cyrillic characters that visually spell out the forbidden request but technically use different Unicode characters. The safety filter checking for specific English phrases might not catch it, but the LLM, trained on diverse text including multiple alphabets, might still understand and respond to the request.

Training Data Poisoning: If emoji-encoded malicious content makes it into an LLM’s training data, the model might learn to recognise and even replicate these encoding schemes. This could result in the model inadvertently helping attackers by generating emoji-encoded malicious payloads or failing to recognise them as threats when analysing suspicious content.

Context Window Manipulation: LLMs have limited context windows (the amount of text they can process at once). Attackers can use invisible Unicode characters to pad inputs, pushing important safety instructions or system prompts out of the model’s effective context whilst keeping malicious instructions within it. The model might then follow attacker instructions without the safeguards that should be governing its behaviour.

Output Encoding Attacks: Even if an LLM correctly identifies malicious content, attackers can request that the output be encoded using emoji or Unicode tricks. The model might comply, creating encoded malicious payloads that bypass downstream security filters. For example, asking an LLM to “translate this command into emoji” could result in the creation of an emoji-based encoding scheme that evades detection.

Jailbreaking and Safety Bypass: The LLM security community has documented numerous “jailbreaking” techniques where carefully crafted prompts cause models to ignore their safety training. Unicode tricks add another dimension to this. Attackers can use direction override characters, invisible spaces, or homoglyphs to craft prompts that appear innocent to automated safety systems but contain hidden instructions that the LLM interprets and follows.

Challenges for AI Security Teams: Defending LLMs against emoji smuggling is particularly challenging because these models are designed to be flexible and understand context across languages and writing systems. Blocking all Unicode would severely limit their utility for international users. Instead, organisations deploying LLMs need to:

  • Implement robust input normalisation before text reaches the model
  • Use multiple layers of content filtering that account for Unicode variations
  • Monitor model outputs for unusual Unicode patterns that might indicate encoding attempts
  • Regularly test models with Unicode-based attack vectors
  • Maintain updated safety training that includes awareness of these techniques

The Detection Problem: Traditional security tools can be configured to flag invisible characters or suspicious Unicode patterns. However, LLMs are probabilistic systems that generate novel outputs. This makes it harder to predict when they might be manipulated into producing emoji-encoded content or responding to Unicode-obfuscated instructions. Security teams need to think about both preventing malicious inputs and detecting problematic outputs.

Real-World Implications: As organisations increasingly rely on LLMs for tasks like code generation, content moderation, customer service, and security analysis, the stakes grow higher. An LLM that can be tricked into generating malicious code through Unicode manipulation, or that fails to identify emoji-smuggled threats in content it’s supposed to be moderating, becomes a liability rather than an asset.

The intersection of emoji smuggling and LLM security represents an emerging area of concern. As these AI systems become more capable and more widely deployed, attackers will continue to probe for weaknesses in how they handle Unicode and interpret encoded content. Organisations must stay vigilant and ensure their AI security strategies account for these evolving threats.

The Challenge for Defenders

Defending against emoji smuggling is tricky because it requires balancing security with functionality. Organisations face several challenges:

International Requirements: Businesses serve global customers and employ international staff. Blocking non-English characters would prevent people from using their actual names or communicating in their native languages. This isn’t just inconvenient; in many jurisdictions, it could be discriminatory.

Performance Concerns: Thoroughly inspecting every character of every piece of text for Unicode tricks requires significant computing power. For high-traffic websites or applications, this can slow things down noticeably.

Evolving Techniques: The Unicode standard contains over 140,000 characters and is regularly updated. Attackers constantly find new, creative ways to exploit this complexity. What works to block attacks today might not catch the techniques used tomorrow.

False Positives: Aggressive filtering can block legitimate content. An email from a Greek customer with a name containing Greek letters might be flagged as suspicious. A message containing many emojis (completely normal in casual conversation) might trigger alerts.

Defensive Strategies

Despite these challenges, organisations can implement effective defences against emoji smuggling. The key is taking a layered approach rather than relying on any single solution.

Input Validation and Normalisation: Systems should normalise Unicode input, converting visually similar characters to a standard form. This helps ensure that “pаypal” (with a Cyrillic ‘а’) and “paypal” (with an English ‘a’) are recognised as attempts to use the same string. For structured data like usernames or email addresses, systems can enforce stricter rules about which characters are allowed.

Context-Aware Security: Different fields need different levels of restriction. A username field might only allow basic English letters and numbers, whilst a comment field can permit a wider range of characters, including emojis. Security controls should adapt to the context rather than applying blanket rules.

Visual Similarity Detection: Advanced systems can detect when Unicode characters are being used to mimic legitimate domains or brands. If someone tries to register a domain that looks almost identical to a major company’s website, the system can flag it for review.

Invisible Character Removal: For most applications, there’s no legitimate reason to include invisible Unicode characters in structured data. Systems can strip these out or flag their presence as suspicious, particularly in fields like usernames, file names, or code inputs.

Monitoring and Anomaly Detection: Rather than trying to block everything suspicious at the gate, organisations can monitor for unusual patterns. A sudden spike in emoji usage in log files, the presence of mixed alphabets in a single field, or zero-width characters appearing in database entries can all trigger alerts for security teams to investigate.

User Education: Technical controls only go so far. Training staff to recognise suspicious URLs (by checking the actual address in their browser, not just what’s displayed), to be cautious about unexpected login requests, and to report unusual behaviour helps catch attacks that slip through automated defences.

Security by Design: When building new systems, developers should consider Unicode handling from the start. This includes using libraries that properly handle normalisation, implementing appropriate validation for each input field, and testing with Unicode attack vectors during security assessments.

What This Means for Different Audiences

For Security Professionals: Emoji smuggling should be part of your threat model. Include Unicode-based attacks in penetration testing, ensure your security tools can detect these techniques, and review how your applications handle Unicode input. This isn’t a theoretical concern; it’s being actively exploited.

For Developers: Don’t assume that checking for suspicious ASCII strings is sufficient. Implement proper Unicode normalisation, validate input based on context, and be aware of how your programming language and frameworks handle Unicode. What you see on screen may not be what’s actually stored or processed.

For Business Leaders: Understand that security isn’t just about detecting known malware signatures or blocking obvious threats. Modern attacks exploit subtle aspects of how systems work. Investment in security tools, training, and secure development practices pays dividends by preventing breaches that could damage reputation and finances.

For Everyday Users: Be sceptical of links, even if they look legitimate. When entering sensitive information, double-check that you’re on the correct website by examining the URL carefully. Be particularly cautious with messages that create urgency or ask you to log in via a provided link.

The Bigger Picture

Emoji smuggling is part of a broader category of attacks that exploit the gap between human perception and machine processing. We see what we expect to see, whilst computers process what’s actually there. Attackers exploit this disconnect.

This isn’t unique to Unicode. Similar principles apply to audio deepfakes (where we hear what sounds like a trusted voice), visual manipulations (where images appear legitimate but are fabricated), and social engineering (where contexts appear trustworthy but are manufactured). The common thread is exploiting trust and perception.

As systems become more sophisticated, so do attacks. The growth of international internet usage and the ubiquity of emoji in modern communication create both opportunities and challenges. We need security solutions that protect without stifling legitimate use, that adapt to new threats whilst maintaining usability, and that account for the complexity of human language and communication.

Conclusion

Emoji smuggling demonstrates that security threats don’t always come from sophisticated zero-day exploits or advanced persistent threats. Sometimes they come from clever misuse of legitimate functionality. A simple emoji or an invisible character can bypass expensive security systems if those systems aren’t designed to handle them.

The good news is that awareness and proper design can mitigate these risks. Organisations that understand the threat, implement appropriate controls, and maintain vigilance can protect themselves effectively. It requires thinking beyond traditional security approaches and considering how attackers might abuse features we take for granted.

As you think about your own organisation’s security, consider asking: How do our systems handle Unicode? Could someone use look-alike characters to impersonate our brand? Are we monitoring for unusual patterns in text input? Could malicious code be hiding in emojis or invisible characters?

These questions might reveal gaps in your defences, but identifying those gaps is the first step towards closing them. In security, the threats we understand and prepare for are far less dangerous than the ones we overlook.

Smiling emoji image photo by chaitanya pillala on Unsplash.

Header image photo by Shubham Dhage on Unsplash.

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Investigation, Opinion

Key Cyber Threat Intelligence Trends to Watch in 2026

Why 2026 Matters for CTI

As organisations enter 2026, cyber threat intelligence finds itself at a critical inflexion point. The threat landscape continues to expand in volume and complexity, but the pressures shaping it are no longer purely technical. Geopolitical instability, regional conflict, and sustained economic uncertainty are increasingly influencing who is targeted, why, and to what end. For businesses, this means cyber risk is now inseparable from broader strategic and operational risk.

At the same time, the pace of technological change continues to accelerate. Artificial intelligence is now firmly embedded on both sides of the threat equation. Adversaries are using AI to scale social engineering, automate reconnaissance, and rapidly adapt tooling, while defenders are racing to apply the same technologies to detection, analysis, and response. This arms race is generating more data, more alerts, and more intelligence than ever before.

Yet quantity is no longer the problem. Many organisations are experiencing intelligence overload, where feeds, reports, and indicators accumulate faster than they can be meaningfully consumed. Decision makers are not asking for more information, but for clearer insight. They want to understand which threats matter, how they are likely to manifest, and what actions should be prioritised in response.

As a result, 2026 represents a decisive shift for cyber threat intelligence. The focus is moving away from collecting more data and towards understanding it better. Success is increasingly defined by context, relevance, and the ability to translate technical detail into actionable judgment. This is less a year of entirely new threats and more a year defined by how existing threats are used, scaled, and adapted to specific targets and circumstances.

In this article, we explore the key trends shaping cyber threat intelligence in 2026, and what they mean for organisations seeking to make informed, risk-based decisions in an increasingly uncertain environment.

AI-Native Threat Actors Become the Norm

By 2026, the use of artificial intelligence by threat actors can no longer be described as experimental or emerging. For many adversaries, AI-enabled tooling is now embedded into everyday operations, shaping how attacks are planned, executed, and refined. Rather than creating entirely new categories of threats, AI is amplifying existing ones by increasing their speed, scale, and apparent sophistication.

One of the most visible impacts is in phishing, pretexting, and broader social engineering activity. AI-generated content allows attackers to produce convincing messages tailored to specific organisations, roles, or even individuals with minimal effort. Language quality is no longer a reliable signal of legitimacy, and pretexts can be rapidly adapted based on open source information, previous engagement, or real-time feedback. This has significantly reduced the cost and skill barrier traditionally associated with effective social engineering.

Malware development has also been accelerated. AI-assisted coding and analysis tools enable faster iteration, allowing threat actors to modify payloads, obfuscation techniques, and delivery mechanisms in near real time. Polymorphism and frequent recompilation mean that identical samples may exist only briefly, limiting the usefulness of traditional signature-based detection and static file indicators. The result is a faster-moving malware ecosystem that is harder to catalogue and track using conventional methods.

Reconnaissance and target profiling are increasingly automated. Threat actors can now use AI to process large volumes of leaked data, scraped content, and technical metadata to identify high-value targets and likely points of weakness. This automation enables more precise targeting while reducing the need for manual research, allowing even smaller or less experienced groups to operate with a level of efficiency previously associated with more capable actors.

Taken together, these developments are blurring traditional distinctions between high-skill and low-skill adversaries. Tools that once required significant expertise to develop or operate are becoming accessible through automation and commoditised services. As a result, lower capability actors can conduct campaigns that appear more polished, more targeted, and more persistent than their underlying skill level would suggest.

For cyber threat intelligence teams, this shift has important implications. Static indicators such as file hashes, domains, and IP addresses are ageing even faster than before, often becoming obsolete within hours or days. While such indicators still have operational value, they can no longer be the primary lens through which AI-enabled activity is understood.

Instead, there is a growing need to focus on behavioural patterns and campaign-level analysis. Understanding how attacks are structured, how lures evolve over time, and how infrastructure is deployed and rotated provides more durable insight than individual technical artefacts. Equally important is tracking the evolution of tradecraft. The key intelligence question is no longer which tool was used, but how it was applied, adapted, and combined with other techniques to achieve an objective.

In 2026, effective threat intelligence depends less on cataloguing tools and more on recognising patterns of behaviour. As AI continues to level the playing field for adversaries, the ability to identify and contextualise these patterns will be central to maintaining meaningful visibility into the threat landscape.

AI-Enabled Tradecraft in Practice

During 2024 and 2025, security researchers documented the use of generative AI tools such as WormGPT and FraudGPT in live phishing and business email compromise campaigns, enabling fluent, highly targeted lures at scale. Microsoft and Google both reported attackers using AI-assisted reconnaissance to tailor phishing based on user roles, organisations, and cloud environments. In parallel, Mandiant and Microsoft observed identity-focused intrusions where domains, payloads, and malware variants rotated faster than traditional indicators could be operationalised. While static indicators decayed rapidly, behavioural patterns such as role-based targeting, cloud-hosted delivery, MFA abuse, and living-off-the-land activity remained consistent.

Content and Format Abuse Outpaces Traditional Detection

As technical controls continue to improve, threat actors are increasingly shifting their focus away from exploiting software vulnerabilities and towards abusing trust in common content formats. By 2026, malicious activity is frequently concealed within files and data types that organisations are structurally inclined to allow, inspect lightly, or prioritise for usability over security.

Content-type smuggling and polyglot files are becoming more prevalent as attackers exploit discrepancies between how systems interpret file formats. A single file may present itself as benign to one control while being parsed differently by another, allowing embedded scripts or payloads to execute downstream. These techniques are not new, but they are now being applied more systematically and at greater scale, particularly in environments that rely on automated content handling.

Common formats such as PDFs, images, emojis, markdown, and compressed archives are increasingly abused as delivery vehicles. PDFs can carry embedded scripts or external references, images can contain hidden data or exploit parsing behaviour, and text-based formats can be manipulated to trigger unexpected interpretation by browsers, email clients, or automated analysis tools. Even elements designed for expression and accessibility, such as emojis, can be repurposed to carry hidden instructions or evade simple content inspection.

Delivery mechanisms are also evolving. Rather than relying solely on direct email attachments or malicious links, attackers are increasingly using trusted SaaS platforms and collaboration tools to distribute payloads. File sharing services, document collaboration platforms, and messaging tools provide a level of implicit trust and are often deeply integrated into business workflows. This makes it harder for both users and security controls to distinguish malicious activity from legitimate use.

These techniques are particularly effective at evading gateway and sandbox-based detection. Many security controls are optimised to analyse standalone files or clearly defined executables, not content that only becomes malicious when rendered in a specific context or combined with user interaction. Sandboxes may fail to replicate the precise conditions required to trigger malicious behaviour, while gateways may prioritise performance over deep inspection of complex or nested formats.

For cyber threat intelligence teams, this trend reinforces the importance of tracking delivery mechanisms as a primary tactic, technique, and procedure. Understanding how malicious content is introduced into an environment often provides more durable insight than focusing solely on the final payload. The same malware family may be delivered through multiple formats and channels, each tailored to exploit specific organisational habits or control gaps.

This also highlights the intelligence value of analysing how malware arrives, not just what it is. Patterns in file types, hosting platforms, and user interaction requirements can reveal actor preferences and campaign objectives that are not visible through static analysis alone. Such insights are particularly valuable for informing detection engineering and user awareness efforts.

Finally, this trend underscores the need for stronger collaboration between cyber threat intelligence teams and email and web security functions. Intelligence on emerging delivery techniques must be translated into practical guidance for those configuring and tuning controls. In 2026, effective defence against content and format abuse depends not only on identifying malicious artefacts, but on understanding and disrupting the pathways through which they are delivered.

Abuse of Trusted Formats and Platforms

During 2023–2025, multiple security vendors reported widespread abuse of PDFs and archive files to deliver malware while bypassing email and web gateways, including campaigns where malicious content was only revealed after user interaction. Microsoft and Google both documented attackers hosting payloads on legitimate SaaS platforms such as OneDrive, Google Drive, and Dropbox, exploiting implicit trust and integration with enterprise environments. Researchers also observed the use of HTML smuggling and polyglot files to evade content inspection by disguising executable behaviour within allowed formats. In many cases, sandbox detonation failed to trigger malicious activity due to environmental checks or delayed execution. These campaigns demonstrated that the most reliable intelligence signal was not the final payload, but the consistent delivery techniques and abuse of trusted platforms, reinforcing the value of tracking delivery mechanisms as a primary tactic.

The Continued Rise of Identity-Centric Attacks

The Continued Rise of Identity-Centric Attacks

As organisations continue to adopt cloud services and remote working models, identity has become the primary control plane for access to systems and data. In 2026, attackers are increasingly targeting identity directly, recognising that compromising credentials or sessions often provides broader and more durable access than exploiting a single technical vulnerability.

One of the most common techniques remains multi-factor authentication fatigue, often referred to as push bombing. By repeatedly triggering authentication prompts, attackers aim to exploit user frustration or inattention, eventually inducing approval of a fraudulent request. While awareness of this technique has grown, it remains effective in environments where controls are permissive or user training is inconsistent.

Token theft and session hijacking are also becoming more prevalent. Rather than capturing usernames and passwords, attackers increasingly seek to obtain valid session tokens, cookies, or authentication artefacts that allow them to bypass interactive login processes altogether. These techniques are particularly effective against cloud services and single sign-on environments, where a compromised token can provide access to multiple applications without further challenge.

The abuse of OAuth applications and cloud identities represents another significant area of risk. Malicious or compromised OAuth apps can be granted persistent access to user data and resources, often with limited visibility once approved. Attackers may also create or manipulate cloud-native identities, such as service principals or managed identities, to establish long-term access that blends into normal administrative activity.

Once access is obtained, many adversaries favour living-off-the-land techniques within cloud environments. By using legitimate tools, built-in administrative functions, and native APIs, attackers can move laterally, escalate privileges, and exfiltrate data while minimising the use of overtly malicious tooling. This approach reduces the likelihood of triggering traditional malware-focused detection and allows activity to appear operationally routine.

For cyber threat intelligence teams, these developments necessitate a shift in focus. Traditional indicators such as IP addresses and domains remain relevant, but they provide limited insight into identity-centric attacks that leverage legitimate infrastructure and services. Greater value lies in understanding patterns of authentication abuse, anomalous access behaviour, and misuse of identity features.

Tracking actor playbooks against identity and access management controls is becoming increasingly important. Intelligence that maps how specific adversaries exploit MFA configurations, token lifetimes, OAuth consent processes, or role assignment models can directly inform defensive priorities. This enables organisations to move beyond generic hardening guidance and focus on the controls most likely to be targeted.

In 2026, effective threat intelligence plays a critical role in shaping identity defence. By translating observed attack patterns into concrete recommendations, CTI teams can help organisations prioritise identity hardening efforts and reduce exposure at what has become the most frequently attacked layer of the modern enterprise.

Identity as the Primary Attack Surface

Between 2023 and 2025, Microsoft, Mandiant, and Okta documented a sustained rise in identity-centric intrusions involving MFA fatigue attacks, token theft, and session hijacking, particularly against cloud-first organisations. Campaigns attributed to financially motivated groups showed repeated push bombing attempts followed by abuse of valid sessions rather than credential reuse. Researchers also reported widespread misuse of OAuth applications, where attackers gained persistent access by tricking users into granting permissions to malicious or compromised apps. Once inside, adversaries frequently relied on living-off-the-land techniques, using native cloud tooling and APIs to blend into normal administrative activity. These cases highlighted the limited value of traditional IP- or domain-based indicators and reinforced the importance of tracking identity behaviour and attacker playbooks against IAM controls.

Ransomware Becomes a Business Model, Not a Malware Type

Ransomware Becomes a Business Model, Not a Malware Type

By 2026, ransomware will be best understood not as a single category of malware, but as a service-driven business model. The technical payload used to encrypt systems is often interchangeable, while the real differentiation lies in how operations are organised, monetised, and sustained. This shift continues to reshape both the threat landscape and the way organisations should approach ransomware risk.

Ransomware-as-a-service ecosystems continue to evolve and mature. Core developers provide tooling, infrastructure, and branding, while affiliates conduct intrusions and deploy payloads in exchange for a share of the proceeds. This model allows rapid scaling, frequent rebranding, and the replacement of disrupted components with minimal impact to overall activity. It also creates a steady flow of new and short-lived variants that complicate traditional tracking.

At the same time, ransomware operations are increasingly decoupled from encryption itself. Data theft and extortion-only models remain prevalent, particularly where reliable backups or operational resilience reduce the impact of encryption. Many campaigns now combine multiple pressure points, including data leaks, regulatory exposure, and direct contact with customers or partners. These hybrid approaches are designed to maximise leverage while reducing technical complexity.

Rebranding and fragmentation further obscure attribution. Groups regularly change names, infrastructure, and public-facing personas in response to law enforcement action or reputational damage. In some cases, operators deliberately adopt the branding or tactics of other groups to mislead victims and researchers. False-flag activity adds further noise, making it difficult to draw conclusions based solely on malware samples or ransom notes.

Targeting is also shifting. While large enterprises remain attractive, mid-sized organisations are increasingly in focus due to perceived gaps in security maturity and incident response capability. Supply chains continue to present valuable opportunities, allowing attackers to leverage trusted relationships to increase reach and impact. These campaigns often prioritise speed and disruption over long-term persistence.

For cyber threat intelligence teams, these trends present both challenges and opportunities. Actor clustering becomes more difficult as tooling and branding fragment, but it also becomes more valuable. Understanding how campaigns relate to one another through shared behaviours, infrastructure management, and operational patterns provides insight that individual malware labels cannot.

This reinforces the need to focus on who is behind an operation rather than which strain is used. Tracking negotiation behaviour, communication style, leak site activity, and pressure tactics can reveal consistent operator fingerprints even as technical components change. Such intelligence is particularly valuable for incident response planning, negotiation strategy, and executive decision-making.

In 2026, effective ransomware intelligence depends on moving beyond file-based analysis and towards a deeper understanding of adversary operations as businesses in their own right. Those who can identify and anticipate how these businesses operate are better positioned to disrupt them and reduce their impact.

Ransomware as an Operational Business

From 2023 to 2025, ransomware groups such as LockBit, ALPHV, and Cl0p were repeatedly observed operating as service-based ecosystems, with affiliates conducting intrusions while core teams managed tooling, infrastructure, and leak sites. High-profile campaigns, including the MOVEit and GoAnywhere mass exploitation events, demonstrated how data theft and extortion could be conducted at scale without relying solely on encryption. Researchers also documented frequent rebranding and fragmentation following law enforcement pressure, complicating attribution based on malware families alone. Across these campaigns, consistent behaviours such as negotiation style, leak site structure, and pressure tactics persisted even as payloads and infrastructure changed. These patterns underscore the value of actor-centric intelligence focused on who is operating, rather than which ransomware strain is deployed.

Geopolitics Drives Threat Actor Priorities

Geopolitics Drives Threat Actor Priorities

In 2026, the influence of geopolitics on the cyber threat landscape is more pronounced than ever. Nation-state and state-aligned actors are not only increasing in activity but are also shaping the broader ecosystem in which financially motivated and ideologically driven groups operate. Cyber operations are now a routine extension of geopolitical competition, conflict, and signalling.

One key trend is the spillover of geopolitical tensions into cyberspace. Regional conflicts, diplomatic disputes, and economic sanctions frequently coincide with surges in cyber activity, ranging from espionage and influence operations to disruptive attacks. These campaigns may not always be directly attributable to a single state, but they often align closely with national interests or strategic objectives.

Critical infrastructure and logistics networks are increasingly attractive targets. Energy, transport, telecommunications, and supply chain management systems offer opportunities for intelligence collection, disruption, and strategic pressure. Even limited or short-lived interference can have outsized economic and political effects, making these sectors a persistent focus for capable adversaries.

Hacktivism continues to play a prominent role, often blurring the boundary between grassroots activism and state-aligned activity. In some cases, hacktivist groups act as proxies or amplifiers, conducting operations that provide plausible deniability while supporting broader strategic aims. In others, state actors deliberately mimic hacktivist tactics to obscure attribution and complicate response decisions.

These dynamics contribute to increasingly blurred lines between cybercrime, espionage, and disruption. Financially motivated groups may be tolerated or tacitly supported when their activity aligns with national interests, while espionage operations may incorporate criminal techniques or infrastructure. This convergence makes simple categorisation of threats less meaningful and increases the risk of misinterpretation.

For cyber threat intelligence teams, this environment elevates the importance of strategic intelligence alongside tactical reporting. Understanding the geopolitical context in which activity occurs is often essential to interpreting intent, likely targets, and potential escalation. Mapping geopolitical events to observed cyber activity can help organisations anticipate periods of heightened risk and adjust their posture accordingly.

Equally important is the ability to communicate uncertainty and intent to leadership. Strategic intelligence rarely offers definitive answers, but it can provide informed assessments and plausible scenarios. In 2026, effective CTI is measured not only by technical accuracy but by its ability to support informed decision-making in a world where cyber activity is increasingly intertwined with global politics.

Geopolitics Shaping Cyber Operations

Between 2022 and 2025, geopolitical events including the war in Ukraine and heightened tensions in the Middle East coincided with spikes in cyber activity targeting government, energy, logistics, and telecommunications sectors. Security firms and government agencies reported coordinated campaigns involving espionage, disruption, and influence operations aligned with national interests. Hacktivist groups emerged rapidly around these conflicts, often amplifying or obscuring state-aligned activity through defacements, data leaks, and denial-of-service attacks. In several cases, financially motivated and politically aligned operations used overlapping infrastructure and techniques, blurring traditional threat categories. These trends highlighted the growing importance of strategic intelligence that links geopolitical developments to cyber activity and communicates intent and uncertainty to decision-makers.

Intelligence Consumers Demand Clarity, Not Just Alerts

Intelligence Consumers Demand Clarity, Not Just Alerts

As cyber threat intelligence becomes more widely consumed across organisations, expectations around how intelligence is delivered are evolving. In 2026, the challenge is no longer access to threat data, but ensuring that alerts and intelligence are timely, relevant, and actionable for their intended audience.

Security teams and decision makers are exposed to a growing volume of alerts, notifications, and intelligence updates. While this flow of information is essential for maintaining situational awareness, it can become difficult to distinguish between background noise and issues that require immediate attention. This has led to increasing demand for clarity alongside coverage.

Rather than simply asking what has been observed, intelligence consumers are asking more targeted questions. They want to understand why an alert matters, how it relates to their environment, and what actions should be considered next. Alerts that are enriched with context, confidence, and clear analytical judgment are far more likely to drive effective response than raw signals alone.

This has reinforced the importance of tying intelligence to risk and impact. When alerts are mapped to threat actors, campaigns, targeting patterns, or likely objectives, they become easier to prioritise and act upon. Intelligence that highlights relevance, such as sector targeting, geographic focus, or alignment with known tradecraft, enables organisations to make faster and more informed decisions.

Narrative also plays an increasingly important role. Even within alert-driven systems, structured explanations and concise assessments help consumers interpret activity and avoid misreading its significance. The ability to combine timely alerting with clear analytical framing is becoming a key differentiator in intelligence delivery.

For CTI providers, this reflects a broader maturity shift from delivering data alone to delivering understanding at scale. Alerts remain a critical mechanism for awareness and response, but their value is maximised when they are supported by consistent analysis and clear articulation of what the intelligence means. In 2026, the most effective intelligence services are those that help customers move confidently from notification to decision.

CTI Tooling Consolidation, Integration, and Automation

The CTI tooling landscape continues to evolve as organisations seek to simplify workflows and extract greater value from the intelligence they consume. By 2026, many teams are consolidating platforms and prioritising solutions that integrate cleanly into existing security operations rather than operating in isolation.

Overlapping tools and fragmented intelligence sources can make it difficult to maintain a coherent view of the threat landscape. As a result, there is growing emphasis on platforms and services that centralise intelligence, reduce duplication, and present information in a consistent and usable format. Integration with SIEM, SOAR, EDR, and email security tooling is increasingly expected rather than optional.

Automation plays a central role in enabling this consolidation. Automated enrichment, correlation, and triage allow large volumes of intelligence to be processed and surfaced rapidly. This is particularly important for alert-driven intelligence delivery, where speed and scale are critical. Automation ensures that alerts arrive with the context needed to support immediate action.

At the same time, expectations around automation are becoming more realistic. While machines excel at processing data and identifying patterns, analytical judgement remains essential for interpreting intent, assessing confidence, and identifying meaningful shifts in adversary behaviour. The most effective intelligence platforms combine automated processing with human-led analysis.

This balance also shapes discussions around return on investment. Customers increasingly expect intelligence tooling to demonstrate clear operational benefit, such as improved detection, faster response, or better prioritisation. Intelligence that is delivered in a form that integrates naturally into security workflows is more likely to achieve this impact.

For CTI teams and providers alike, a key consideration is deciding what should be automated and what should remain analyst-driven. Repeatable processes and large-scale data handling benefit from automation, while assessments of intent, relevance, and strategic significance continue to rely on human expertise.

In 2026, the enduring value of experienced analysts is not diminished by automation but amplified by it. By pairing scalable delivery mechanisms with consistent analytical oversight, CTI providers can deliver intelligence that is both timely and trusted. This combination is central to meeting rising customer expectations in an increasingly complex threat environment.

What This Means for CTI Teams in 2026

Taken together, these trends point to a clear evolution in how cyber threat intelligence teams must operate in 2026. The challenge is not a lack of data or tooling, but ensuring that intelligence capability is aligned with real organisational needs and outcomes. Teams that adapt their focus and ways of working will be best placed to deliver sustained value.

First, there is a renewed need to invest in analytical skills alongside technology. Tooling and automated alerting provide essential scale and coverage, but they do not replace the ability to assess relevance, weigh confidence, and draw meaningful conclusions. Developing analysts who can interpret complex activity, recognise patterns over time, and communicate insight clearly remains one of the most effective ways to improve intelligence outcomes.

Second, collection should be increasingly guided by clearly defined priority intelligence requirements. Rather than attempting to monitor everything equally, effective CTI teams focus on the threats, actors, and techniques most relevant to their organisation or customers. Well-defined PIRs help shape what data is collected, how it is analysed, and how it is delivered, ensuring that intelligence production remains purposeful rather than reactive.

Strong relationships across the security and business landscape are also essential. CTI does not operate in isolation, and its value is maximised when it is closely connected to security operations, incident response, identity and access management, and senior leadership. Regular engagement with these stakeholders helps ensure that intelligence outputs align with detection needs, response priorities, and strategic concerns.

Finally, success in 2026 is increasingly measured by influence rather than output. The most effective CTI teams are those that can demonstrate how intelligence has informed decisions, shaped defensive priorities, or enabled faster and more confident responses. Reports and alerts remain important delivery mechanisms, but their true value lies in the decisions they support.

For CTI teams navigating an increasingly complex threat environment, these principles provide a practical foundation. By combining strong analytical capability, focused collection, collaborative working, and outcome-driven measurement, intelligence teams can remain relevant and impactful in the year ahead.

Conclusion: The Evolution of CTI

Cyber threat intelligence in 2026 is evolving rapidly. What was once largely a support function is increasingly a strategic enabler, providing insight that shapes decisions across security operations and organisational leadership. Threats are faster, more complex, and noisier than ever, driven by automation, AI, and shifting geopolitical pressures.

In this environment, the differentiators for effective intelligence are context, clarity, and credibility. Understanding not just what is happening, but why it matters and how it affects the organisation, is what turns data into actionable insight. Teams that can provide this perspective, supported by robust analytical capability and integrated tooling, will be best placed to help organisations anticipate, prioritise, and respond to evolving threats.

2026 will not be defined by new types of threats alone, but by the ability of intelligence teams to interpret them, communicate their significance, and drive meaningful action. In this way, cyber threat intelligence will continue to move from reactive observation to proactive influence, ensuring its central role in organisational resilience and security strategy.

"Behind
Investigation, Opinion

Behind the Mask: Creating and Maintaining Sock Puppet Accounts for Online Research

When conducting online research or gathering open-source intelligence (OSINT), it is often necessary to observe or interact with digital spaces without revealing your true identity. This is where sock puppet accounts come into play. A sock puppet is a fictitious online identity created to access information, join closed groups, monitor activity, or engage with targets while protecting the researcher’s real identity and intent.

Used properly, sock puppets are an essential part of an investigator’s toolkit. However, their creation and use come with both ethical and legal responsibilities. Misuse can lead to legal consequences, reputational damage, or compromised investigations. Practitioners must always follow legal guidance and act within clearly defined ethical boundaries.

In this blog, we will explore how to plan, create, and maintain effective sock puppet accounts for OSINT purposes. We will discuss key operational security (OPSEC) measures, common pitfalls to avoid, and strategies for maintaining a convincing online persona over time. Whether you are new to this practice or looking to refine your approach, this guide will help you lay a solid foundation for safe and responsible online research.

What Is a Sock Puppet Account?

A sock puppet account is a false or alternate online identity used to conceal the true identity of the user behind it. In the context of online investigations and intelligence gathering, sock puppets allow researchers to access and monitor digital spaces without drawing attention to their real-world affiliations or investigative purpose.

These accounts are beneficial in OSINT investigations where anonymity is critical. They may be used to:

  • Access private or semi-restricted forums and groups
  • Observe conversations on social media without alerting subjects
  • Collect threat intelligence from Dark Web marketplaces or closed communities
  • Engage with individuals or groups in a way that does not compromise operational security

While sock puppets can be powerful tools, their use must always be underpinned by legal and ethical awareness. Investigators should never use false identities to entrap, manipulate, or harass individuals. The goal is passive information gathering, not interference or provocation. Moreover, laws governing online impersonation, data protection, and computer misuse vary between jurisdictions, and it is the investigator’s responsibility to ensure compliance.

Wherever possible, work within organisational policies, maintain internal approval processes for sensitive research, and document all actions for accountability. Ethical OSINT hinges not only on what can be done, but on what should be done.

Planning Your Sock Puppet Strategy

Before creating a sock puppet account, it is essential to define a clear objective. What do you need the account to do? Your goal might be to passively observe a forum, monitor a social media group, or engage with a specific individual or community. The purpose of the account will shape every decision that follows, from the choice of platform to the construction of your online persona.

Understanding your target environment is a crucial part of this planning stage. Different platforms have different norms, verification processes, and levels of scrutiny. A persona that appears credible on Reddit might not be believable on LinkedIn. Consider regional factors as well: language, time zone, and cultural references all contribute to the authenticity of an account. An inconsistency in these details can quickly arouse suspicion.

With your objective and environment defined, you can begin to craft a suitable cover story. This should include a basic biography, a plausible location, interests relevant to the communities you plan to interact with, and a consistent tone of voice. Keep the persona simple, but detailed enough to withstand casual scrutiny. Avoid unnecessary complexity, which can increase the risk of contradictions or mistakes.

A well-planned sock puppet starts long before the account is created. By aligning your objectives with your operational context and building a realistic backstory, you lay the groundwork for a credible and sustainable online identity.

Creating the Sock Puppet Account

Once your planning is complete, the next step is to create the sock puppet account itself. This process involves selecting the right platform, crafting a believable identity, and ensuring that your setup maintains strong operational security from the outset.

Choosing the Right Platform

Select your platform based on the objective of the investigation. If you need to observe professional activity or gather company intelligence, LinkedIn might be appropriate. For community discussions, Reddit or Discord may be more useful. For threat intelligence gathering, forums or encrypted messaging apps could be more suitable. Each platform has its own registration process, verification requirements, and user expectations, all of which must be considered.

Crafting a Believable Identity

A convincing sock puppet needs to pass casual inspection. Start with a realistic username and a dedicated email address that fits your persona. Avoid using anything that resembles your real name or any identifiers linked to your organisation.

  • Profile photo: Use AI-generated images or copyright-free alternatives. Tools like ThisPersonDoesNotExist or Generated Photos can be helpful, but check for anomalies that might raise suspicion.
  • Biography and interests: Write a brief, plausible bio that fits the persona and platform. Add relevant interests or affiliations to make the account appear active and authentic.
  • Posting behaviour: Mirror the tone, grammar, and posting frequency typical for the platform and user type. If your persona is a 30-year-old from Manchester, for example, ensure the language and topics reflect that identity.
  • Language consistency: Stick to one language and dialect throughout. Switching between different styles or regions can be a clear indicator of inauthenticity.

Acquiring a Clean IP

To prevent your real identity or location from being linked to the sock puppet, use a clean and separate IP address. A reputable VPN or proxy service is essential, and in some cases, a dedicated virtual machine or separate device should be used. Avoid logging in to real accounts or using your usual browser within the same environment, as cross-contamination can compromise the entire operation.

Account creation is not just about filling in a form. Every detail, from your profile picture to your browser setup, contributes to the believability and security of the puppet. Take your time, document each step, and treat the identity as if it were real.

OPSEC Considerations

Operational Security (OPSEC) is critical to the effective use of sock puppet accounts. Without proper precautions, it is easy to leave digital traces that link back to your real identity or organisation. To maintain credibility and protect yourself, you must build strong habits around device use, network hygiene, and identity compartmentalisation.

Device and Network Isolation

Always use a dedicated environment for sock puppet activity. This might be a virtual machine (VM), a separate user profile, or an entirely distinct physical device. The key is to ensure that no personal data, saved credentials, or browsing habits from your real identity carry over into the puppet’s digital footprint. Similarly, connect via a trusted VPN or proxy with a location appropriate to the persona. Never use your home or work IP address when managing sock puppets.

Avoiding Contamination

Cross-contamination with real accounts is one of the most common OPSEC failures. Use a clean browser instance with no saved cookies, autofill data, or extensions that may reveal identifying information. Consider using privacy-focused browsers or containerised browsing sessions to isolate activity. Disable features like browser synchronisation or automatic logins, which could leak personal credentials.

Using Burner Phones and Anonymous Email

When platforms require phone numbers for verification, use a burner device or a secure, anonymised SMS service, provided it complies with legal and policy requirements. Similarly, choose privacy-conscious email providers such as ProtonMail or Tutanota. The email address should align with the puppet’s identity and not reference any real-world details.

Password and Account Recovery Separation

Treat sock puppets as standalone entities. Use unique, complex passwords for each account and manage them using a secure password manager. Keep recovery options consistent with the identity—never link your real email or phone number. If using recovery questions, invent answers that match the puppet’s backstory and document them securely.

Logging and Documentation

Maintain secure records of your sock puppets, including account details, access credentials, personas, activity logs, and creation dates. This helps track usage over time, identify potential compromises, and safely retire or rotate identities when needed. Store this information in an encrypted format or within a secure password management tool.

Sock puppet OPSEC is not about one-time precautions—it requires ongoing discipline. A single mistake can expose your identity or compromise the entire investigation. Take a cautious, methodical approach and revisit your OPSEC practices regularly.

Maintaining Sock Puppets Over Time

Creating a sock puppet is only the beginning. To remain credible and useful over time, the account must appear active, consistent, and authentic. Dormant or obviously artificial profiles are more likely to be flagged by platforms or ignored by the communities you are trying to observe. Maintaining a sock puppet means simulating the behaviour of a genuine user, without attracting unnecessary attention.

Simulating Real Behaviour

Regular interaction is key to building a believable presence. Depending on the platform, this might include:

  • Liking or sharing posts
  • Following relevant accounts or joining groups
  • Commenting or replying in a manner consistent with the persona

These interactions should be contextually appropriate and contribute to the puppet’s credibility. For example, a user who claims to be interested in cybersecurity might follow industry influencers, comment on relevant articles, or share news stories.

Scheduling Realistic Activity Patterns

Sock puppets should reflect normal online behaviour. Consider the timezone and daily schedule of the persona. If your puppet claims to be based in Berlin, it would be unusual for them to post at 3 a.m. local time. Avoid excessive or erratic posting, which can appear automated or suspicious. A light but consistent activity pattern over time is more convincing than bursts of high engagement.

Avoiding Automation Red Flags

Some platforms are aggressive in detecting and removing accounts that behave like bots. Avoid scripted or repeated actions, especially immediately after account creation. Do not mass-follow users or copy-paste identical comments across threads. Behave like a real person—slow, deliberate, and occasionally imperfect.

Regularly Updating Profile Content

Real users update their profiles from time to time. Refresh your puppet’s bio, add a new interest, or change a profile picture occasionally to reflect life events or shifting interests. These subtle changes reinforce the illusion of an active, evolving online identity.

Ultimately, a successful sock puppet account blends in. It should quietly accumulate a digital footprint that supports its cover story and gives you access to the information you need, without ever drawing attention to itself.

Risks, Red Flags, and Account Burnout

Even well-crafted sock puppets carry risk. Platforms continue to improve their ability to detect suspicious behaviour, and users themselves may flag accounts that appear inauthentic. Understanding common warning signs and knowing when to retire or rotate an identity is key to maintaining long-term operational capability.

Common Ways Sock Puppets Get Flagged or Banned

Sock puppets may be suspended or deleted for a range of reasons, including:

  • Logging in from multiple geographic locations in a short space of time
  • Sudden spikes in activity (e.g. mass liking, following, or posting)
  • Use of stock or AI-generated profile images that resemble known fake accounts
  • Repeated use of the same contact details, browser fingerprint, or device setup
  • Lack of meaningful interaction or organic growth over time

Even a single policy violation can draw scrutiny, particularly on mainstream social media platforms where automated systems are quick to act.

Avoiding Repetitive Patterns Across Accounts

If you operate multiple sock puppets, ensure that each has a unique and independent identity. Reusing the same backstory, writing style, or image sources across accounts can make them easier to detect and link together. Separate devices, email addresses, and behavioural traits help to isolate each puppet and reduce the risk of a cascading compromise.

When to Retire a Puppet and How to Replace It Safely

No sock puppet should be considered permanent. If an account is inactive, becomes untrustworthy, or begins attracting unwanted attention, it is often safer to retire it than to try and recover its credibility. Before deletion, remove any content that could be linked to other operations. Keep a log of why it was retired, and plan how a replacement will fill the same role with improved safeguards.

Having Backup Identities Ready

To ensure continuity, it is good practice to maintain a small number of standby identities.  This is sometimes referred to as a “puppet farm”. These can be developed gradually in the background, gaining basic credibility over time, so they are ready to use when needed. In some cases, it may also be appropriate to establish layered personas, where one puppet supports or interacts with another to enhance realism.

Maintaining sock puppets is an operational task that requires regular attention. The digital landscape shifts constantly, and even the most convincing puppet may eventually outlive its usefulness. Being prepared to adapt is vital.

7. Tools and Resources

Successful sock puppet operations depend not only on planning and technique, but also on using the right tools to support anonymity, security, and realism. The following categories highlight essential resources for anyone managing online personas, with emphasis on privacy-focused solutions.

VPNs and Secure Browsers

To prevent IP address leaks or location-based flags, always connect through a reliable virtual private network (VPN). Services such as Mullvad or Proton VPN offer privacy-focused features without logging user activity. In addition, using secure or privacy-hardened browsers, such as Firefox with privacy containers, Brave, or Tor Browser, can help prevent tracking and cross-contamination between real and sock puppet identities.

For advanced operations, consider launching sock puppets within secure environments such as Tails OS or a hardened virtual machine to reduce the digital footprint even further.

Image Generation Tools

Choosing a believable profile image is vital. AI-generated photo tools like ThisPersonDoesNotExist or Generated.Photos create unique images that are not traceable to real people, reducing the risk of impersonation claims. However, these images should be reviewed carefully for visual anomalies that might suggest they are artificial. Alternatively, use licence-free photo repositories where permitted.

Secure Email Services

Every puppet should have its own email address from a secure, privacy-conscious provider. Services such as ProtonMail, Tutanota, or Mailfence are widely used for this purpose. Avoid mainstream providers that require phone verification or link accounts to existing profiles. Where possible, create the email account using the same VPN and device you plan to use for the puppet itself.

Password and Identity Managers

Managing multiple identities requires strict separation and secure record-keeping. Tools like Bitwarden, KeePassXC, or 1Password can be used to store login details, backstory notes, recovery options, and activity logs in an encrypted format. Avoid reusing passwords or security questions across accounts, and clearly label each identity to avoid mistakes.

Burner Phone and SMS Services

Some platforms require phone number verification. Where legally permitted, use burner phones or temporary SMS services to meet this requirement. Options include physical SIMs with disposable devices or online services such as MySudo or Silent Link, though reliability and legality vary by region. Never use your personal or work number under any circumstances.

A well-prepared toolkit makes managing sock puppets more secure, efficient, and scalable. Review your tools regularly, keep backups where needed, and ensure you remain up to date with changes in platform behaviour or verification processes.

Final Thoughts and Best Practices

Sock puppet accounts are powerful tools for legitimate online research. When used responsibly, they enable investigators, analysts, and researchers to access vital information, monitor digital threats, and engage with online communities without exposing their true identity. However, with this capability comes a significant ethical and operational responsibility.

These accounts should never be used to deceive, manipulate, or harm individuals. Their purpose is to observe, gather intelligence, and support investigations that serve the public interest or protect organisations from threats. Operating within legal boundaries and upholding professional standards is essential.

The digital landscape is constantly changing. Platforms evolve, detection methods improve, and user behaviour shifts. This means sock puppet strategies must also be regularly reviewed and refined. What works today may not be effective tomorrow, so ongoing learning and adaptation are key to maintaining both access and security.

Finally, any organisation or individual engaging in this kind of work should develop their own standard operating procedures (SOPs). These should include clear guidelines for planning, creation, use, and retirement of sock puppet accounts. Testing identities in controlled environments before deploying them for real investigations can also help identify weaknesses before they become liabilities.

Used with care, discipline, and a strong ethical framework, sock puppets can provide valuable insight while keeping investigators safe and discreet.

Red flag photo by Paolo Bendandi and sock puppet photo by Natalie Kinnear.

"Cyber
Investigation, Opinion

Beyond the Dark Web: Where Threat Actors Operate

The “dark web” has become something of a buzzword in recent years, often portrayed as the hidden underworld of the internet where cybercriminals operate in complete anonymity. For many, it conjures images of secret marketplaces, illicit data dumps, and hard-to-trace communications — all out of reach from the average internet user.

Because of this perception, it is a common misconception that all threat actor activity takes place exclusively on the dark web. While it certainly plays a role in enabling criminal operations, the truth is far more complex. Today’s threat actors are increasingly making use of platforms that are readily available, user-friendly, and in many cases, completely legal.

Much of their coordination, recruitment, and even data leakage now takes place in plain sight — across encrypted messaging apps, public forums, and mainstream social media platforms. Understanding where these actors truly operate is critical for any organisation looking to stay ahead of the threat landscape.

The Evolving Landscape of Threat Actor Platforms

The way threat actors communicate and coordinate has shifted significantly in recent years. Once heavily reliant on hidden services accessed through the Tor network, many cybercriminals are now embracing more accessible, mainstream platforms to conduct their activities.

This change has been driven by several key factors. One of the most prominent is the increased pressure from law enforcement. High-profile takedowns of dark web marketplaces such as AlphaBay and Hydra have disrupted long-standing criminal ecosystems, forcing actors to reconsider where and how they operate.

At the same time, modern platforms offer features that make them attractive to malicious users. Encrypted messaging apps provide a level of privacy that rivals, and in some cases exceeds, what is available on the dark web. Public forums and chat platforms are easy to access, require minimal technical knowledge, and can reach large audiences quickly.

For cybercriminals, scale and convenience matter. Hosting content on widely used services allows them to cast a broader net, whether they’re distributing stolen data, selling malware, or recruiting new affiliates. The lines between the open internet and covert criminal spaces are increasingly blurred, making it more difficult for defenders to track activity using traditional dark web monitoring alone.

Alternative Threat Actor Channels

While the dark web still plays a role in cybercriminal operations, many threat actors now prefer more accessible and user-friendly platforms. These alternatives offer speed, scalability, and often a surprising degree of anonymity — all without the need for specialised browsers or infrastructure. Below are some of the most commonly used non-dark web channels.

Telegram

Telegram has become a go-to platform for cybercriminals. With its end-to-end encryption, support for large group chats, and the ability to create private or public channels, it offers the ideal environment for discreet coordination at scale.

Threat actors use Telegram to:

  • Leak stolen data and documents
  • Advertise and sell credentials or access to compromised systems
  • Host scam pages or phishing kits
  • Organise affiliate networks or ransomware-as-a-service (RaaS) operations

Its minimal moderation and vast global user base make it a particularly attractive choice for cybercrime groups.

Discord and Other Chat Platforms

Originally designed for online gaming communities, Discord has evolved into a full-featured communication tool with support for text, voice, and private servers. Unfortunately, these same features have also made it a popular haven for fraudsters and cybercriminals.

Threat actors use Discord to:

  • Create closed communities centred around fraud, hacking tools, or data leaks
  • Share resources in “plug” communities — often focused on carding, identity theft, or botnet services
  • Coordinate attacks or distribute malware through seemingly innocuous links

Other platforms such as Tox, Matrix, and IRC-based services are also used, albeit with smaller user bases.

Surface Web Forums

Despite the risks of being in plain sight, many cybercrime forums continue to operate openly on the surface web. These forums are often language-specific or focused on particular sectors, such as financial fraud, social engineering, or credential stuffing.

They are typically used to:

  • Trade tools, tactics, and stolen data
  • Post tutorials or share exploit code
  • Vet and recruit participants for more private activities

Some forums operate with limited moderation or are hosted in jurisdictions with lax enforcement, allowing them to persist despite ongoing attention from security professionals.

Social Media (Twitter/X, Facebook, etc.)

Social media platforms remain surprisingly popular for certain types of threat actor activity. On services like Twitter/X, Facebook, and even LinkedIn, cybercriminals can quickly build audiences, push propaganda, or leak stolen information to make a statement.

Common uses include:

  • Publicly claiming responsibility for attacks or breaches
  • Promoting data leaks to gain notoriety or apply pressure to victims
  • Running influence campaigns or disinformation efforts
  • Recruiting low-level actors or collaborators

While these platforms generally respond quickly to takedown requests, the speed at which content can be published and spread makes them a persistent threat vector.

Paste Sites and Temporary File Hosts

Pastebin-style sites and ephemeral file hosting services continue to be used by cybercriminals to share content without needing to manage infrastructure. These services are often exploited to distribute:

  • Malware payloads
  • Indicators of compromise (IOCs)
  • Stolen credentials or internal documentation

Examples include Pastebin, Ghostbin, file.io, and anonfiles (when active). Their simplicity and temporary nature make them appealing for one-off drops or fast-moving campaigns.

Why the Shift Away from the Dark Web?

While the dark web once provided the primary infrastructure for cybercriminal marketplaces and forums, it has become a less attractive option for many threat actors. A combination of practical challenges and strategic advantages has led to a growing preference for mainstream and surface-level platforms.

One of the key drivers behind this shift is the increasing success of global law enforcement operations. High-profile takedowns such as AlphaBay, Hansa, and Hydra have not only dismantled major criminal marketplaces but also sown distrust within dark web communities. With undercover operations and seizures now a recurring threat, many actors perceive mainstream platforms as less risky in terms of operational security, particularly when combined with disposable accounts and encrypted messaging.

Technical reliability is another issue. Dark web services can suffer from poor uptime, slow performance, and hosting instability. These problems make it harder for threat actors to run consistent operations or maintain communication, especially when compared to the seamless experience offered by platforms like Telegram or Discord.

Accessibility also plays a major role. Mainstream platforms are far easier to use and require no special configuration or tools. Anyone with a smartphone can join a Telegram group or browse a fraud forum hosted on the surface web. This lowers the barrier to entry for newer or less technically skilled actors, fuelling growth in cybercriminal communities.

Finally, these platforms offer scale. Social media, public channels, and open forums provide instant access to large audiences, whether for pushing stolen data, coordinating campaigns, or recruiting collaborators. The potential for amplification far exceeds what is typically possible within the confines of the dark web.

For all these reasons, the dark web is no longer the sole or even primary location for cybercriminal activity. Threat actors are adapting to a broader, more dynamic digital environment, and defenders must do the same.

Implications for Threat Intelligence Teams

As threat actors diversify their platforms, the scope of effective cyber threat intelligence (CTI) must evolve accordingly. Relying solely on dark web monitoring is no longer sufficient. Instead, teams must broaden their visibility to include the various surface and semi-private spaces where cybercriminal activity increasingly takes place.

Monitoring closed channels such as Telegram groups, Discord servers, and niche forums has become essential. However, these spaces are often harder to access and require greater care in terms of operational security (OPSEC). Joining or observing these groups can carry significant risk if not done properly. Analysts must use hardened environments, anonymous accounts, and clear protocols to avoid detection or legal exposure.

Language skills and cultural awareness are also becoming increasingly important. Many cybercrime communities operate in non-English languages and use regional slang or coded terminology. Without this context, valuable intelligence can be missed or misinterpreted. Investing in native language analysts or translation tools can dramatically improve coverage and insight.

The scale and speed at which content is published across platforms make manual monitoring impractical. As such, automation is vital. Tools that scrape and index Telegram posts, track mentions on social media, or flag emerging IOCs can help intelligence teams respond quickly and reduce the chance of missing key developments.

Ultimately, the shift in threat actor behaviour demands a shift in defender strategy. The more fragmented and accessible the threat landscape becomes, the more agile and well-equipped CTI teams need to be in order to stay ahead.

Case Examples

LockBit’s Use of Telegram for PR and Leak Amplification (2024)

In early 2024, after suffering internal leaks and DDoS attacks against their dark web leak site, the LockBit ransomware group turned to Telegram to regain control of their narrative. The group created public Telegram channels to share statements, leak victim data, and coordinate with affiliates. This move not only ensured continuity during technical outages but also expanded their audience beyond the dark web’s limited reach.

Telegram’s encryption, ease of access, and built-in forwarding features allowed LockBit to amplify their message rapidly, including to journalists, researchers, and rival threat actors. It showcased a tactical shift: using mainstream tools as a parallel infrastructure for both influence and extortion pressure.

“Infinity Stealer” Malware Sold via Discord and GitHub (Mid–2023 Onwards)

Infinity Stealer, a malware strain targeting browser credentials and crypto wallets, began circulating heavily in 2023 via non-dark web platforms, notably Discord and GitHub. The malware was marketed in private Discord servers where prospective buyers were vetted and provided updates. GitHub repositories were used to host payloads, configuration templates, and instructions, often disguised as open-source tools.

This campaign highlights how cybercriminals are bypassing traditional marketplaces entirely, instead using legitimate platforms for both sales and delivery infrastructure. Discord’s private server structure and GitHub’s reputational cover enabled the operators to fly under the radar while still reaching a large pool of technically capable users.

Conclusion

The dark web remains a valuable source of cyber threat intelligence — but it is no longer the whole story. As cybercriminals adapt to a shifting digital landscape, they are increasingly leveraging open and semi-closed platforms like Telegram, Discord, and even mainstream social media to conduct and promote their activities.

For CTI teams, this evolution demands a broader approach. Effective monitoring now extends beyond Tor and onion domains to include a mix of channels, each with its own risks, nuances, and intelligence value. It also requires enhanced OPSEC, linguistic awareness, and the integration of automation tools to track activity at scale.

By recognising these trends and adapting monitoring strategies accordingly, defenders can stay better aligned with the current threat environment — one that is faster, more fragmented, and no longer confined to the shadows.

"Why
Investigation, Opinion

Why Hackers Hack: Exploring What Motivates Cybercriminal Activity

Cybercrime continues to rise in scale, complexity and impact, affecting individuals, businesses and governments alike. While much attention is given to how attacks happen, it’s just as important to ask why they occur in the first place. Understanding what motivates attackers is a crucial part of building an effective defence.

So, why do hackers hack?

Some are driven by financial gain, while others act on behalf of a nation-state or in support of a political cause. There are those motivated by revenge or personal challenge, and others who simply exploit opportunities because they can.

In this post, we explore the key motivations behind cybercriminal activity, helping you better understand the intent behind the threat and its implications for your organisation’s security posture.

Financial Gain

For many cybercriminals, money is the primary motivator. The vast majority of cybercrime is financially driven, with threat actors seeking to extract value from individuals, businesses or governments through theft, fraud or extortion.

Ransomware is perhaps the most well-known example. Attackers encrypt a victim’s data and demand payment, usually in cryptocurrency, in exchange for the decryption key. The rise of Ransomware-as-a-Service (RaaS) has made these attacks more accessible, allowing less technically skilled criminals to launch sophisticated campaigns using tools developed by others.

One of the most notorious examples of financially motivated cybercrime is Evil Corp, a Russia-based cybercrime group responsible for developing and distributing the Dridex banking Trojan and BitPaymer ransomware. The group, led by Maksim Yakubets, has been linked to attacks that have caused hundreds of millions of pounds in damages globally. According to the U.S. Department of the Treasury, Yakubets was allegedly tasked by Russian intelligence to conduct espionage operations alongside his cybercriminal activities. He is known not just for the scale of his crimes, but also for flaunting his wealth—reportedly driving a Lamborghini with a personalised number plate that reads “THIEF”.

Phishing and business email compromise (BEC) are also common financially motivated attacks. These techniques are designed to trick victims into handing over login credentials, payment details or other sensitive information that can be monetised directly or resold on dark web marketplaces. The FBI has reported billions of dollars in losses from BEC schemes, which often involve attackers impersonating executives or suppliers to redirect large financial transactions.

What’s particularly concerning is how mature and professionalised the cybercriminal ecosystem has become. Online forums and marketplaces, often hosted on the dark web, serve as thriving hubs where criminals buy and sell tools, data and services. This includes malware, exploit kits, stolen credentials and even technical support for other attackers. Some actors specialise in initial access, others in data theft or extortion, and many operate purely as brokers or facilitators.

As a result, modern cyberattacks are rarely the work of a lone hacker. Instead, they often involve multiple actors working together across a decentralised and anonymous marketplace. For a relatively low cost, almost anyone can purchase the tools and expertise needed to carry out a breach.

With high rewards and limited risk in many jurisdictions, financially motivated cybercrime remains one of the most significant threats facing organisations today.

Ideological or Political Motivation (Hacktivism)

Not all cybercriminals are driven by profit. Some are motivated by political beliefs, social causes or ideologies. These individuals or groups, often referred to as hacktivists, use hacking as a form of protest, aiming to disrupt, expose or embarrass organisations and governments they oppose.

One of the most recognisable hacktivist collectives is Anonymous, a loosely organised group known for its cyber campaigns against governments, corporations and extremist groups. Their activities have ranged from distributed denial of service (DDoS) attacks on financial institutions, to leaking sensitive documents from law enforcement agencies and political bodies.

Hacktivism has also played a prominent role in modern conflicts. In the early days of the Russia–Ukraine war, groups on both sides of the conflict engaged in cyber operations. Ukrainian-aligned actors, including the so-called IT Army of Ukraine, targeted Russian government websites and media outlets with defacements and DDoS attacks. Meanwhile, pro-Russian hacktivist groups like Killnet have launched attacks against European infrastructure in retaliation for political support of Ukraine.

These operations are not always highly technical, but they can be disruptive and attention-grabbing. For example, in 2022, Killnet claimed responsibility for attacks on several websites belonging to airports, healthcare providers and public institutions across Europe, using basic but effective DDoS techniques.

Hacktivism can blur the line between political protest and criminal activity. While some view it as a legitimate form of dissent in the digital age, it often involves illegal access, data leaks or service disruption, and can escalate geopolitical tensions or cause collateral damage to innocent third parties.

For defenders, politically motivated attacks pose a unique challenge. They may not follow the typical patterns of financially driven crime, and their targets can shift quickly based on current events, perceived injustices or ideological trends.

State-Sponsored Espionage

Some of the most advanced and persistent cyber threats come not from criminals seeking profit, but from nation-states pursuing strategic objectives. These attacks are often aimed at gathering intelligence, disrupting rivals, or gaining long-term access to critical systems. Unlike financially motivated actors, state-sponsored groups tend to operate with significant resources, patience and stealth.

These threat actors—often referred to as Advanced Persistent Threats (APTs)—typically target government departments, defence contractors, critical national infrastructure, and major corporations. Their goal may be to steal sensitive data, conduct surveillance, interfere with democratic processes, or enable future sabotage.

A prominent example is APT29, also known as Cozy Bear, a group linked to Russia’s Foreign Intelligence Service (SVR). They have been implicated in numerous high-profile intrusions, including the 2020 SolarWinds supply chain attack, which compromised several US federal agencies and global private sector organisations. The operation was notable for its sophistication and subtlety, remaining undetected for months.

Similarly, APT10, associated with China’s Ministry of State Security, was involved in an extensive global cyber espionage campaign targeting managed service providers (MSPs). By compromising these third-party IT providers, APT10 was able to access a wide range of downstream client networks, including government and corporate systems in the UK, US and beyond.

Unlike typical cybercriminals, these groups are often protected by their host governments and operate with impunity. They may also work in parallel with criminal organisations, blurring the lines between state and non-state activity. For example, some ransomware attacks have been linked to actors with suspected ties to nation-states, suggesting a dual-purpose intent: generating revenue while causing strategic disruption.

The motivations behind state-sponsored cyber operations are diverse, ranging from political influence and military advantage to intellectual property theft and economic gain. These campaigns are rarely random; they are calculated, well-resourced and long-term in nature.

For organisations, this means traditional defences may not be enough. Combating espionage-level threats requires a heightened focus on detection, incident response and threat intelligence, particularly for those in sensitive sectors.

Corporate or Industrial Espionage

Businesses, particularly those with valuable intellectual property and trade secrets, are prime targets for corporate or industrial espionage. Cybercriminals and competing organisations alike seek to gain an unfair advantage by stealing sensitive data related to research and development (R&D), product designs, strategic plans or proprietary technologies.

This type of espionage often overlaps with state-sponsored cyber operations, where nation-states target foreign companies to bolster their own industries or military capabilities. A notable example is the Operation Aurora campaign, uncovered in 2010, where threat actors believed to be linked to China targeted Google and dozens of other major companies. The attackers aimed to steal intellectual property and gain access to corporate networks.

Similarly, in 2021, the US Department of Justice indicted members of a Chinese hacking group known as APT41 for conducting widespread cyber intrusions into video game companies and technology firms, stealing source code and proprietary information to benefit commercial interests.

R&D-heavy sectors such as biotechnology, aerospace, automotive and software development face particularly high risks. The theft of trade secrets not only undermines a company’s competitive edge but can also result in substantial financial losses and damage to reputation.

Unlike typical financially motivated attacks, corporate espionage campaigns are usually stealthy and meticulously planned. Attackers may maintain prolonged access to compromised networks, gathering intelligence over months or even years to extract maximum value.

Organisations must therefore prioritise safeguarding their intellectual property through robust cybersecurity measures, employee awareness, and stringent access controls. Collaboration with industry partners and government agencies can also help in detecting and mitigating these sophisticated threats.

Personal Challenge or Prestige

For some hackers, the motivation is less about money or politics and more about curiosity, thrill-seeking, or the desire for recognition within their communities. These individuals often see hacking as a puzzle to be solved or a challenge to be conquered, gaining personal satisfaction and prestige among peers.

This motivation is particularly common among younger or amateur hackers, sometimes referred to as “script kiddies”, who may lack advanced skills but are eager to prove themselves by exploiting vulnerabilities or defacing websites. The hacking community online—including forums, social media groups and dark web marketplaces—can foster this behaviour, offering a platform for sharing exploits, bragging rights and reputation-building.

A notable example is the hacktivist group LulzSec, which gained international attention in 2011 through a series of high-profile attacks targeting organisations like Sony, the CIA, and PBS. Their actions were largely driven by the desire to embarrass their victims and entertain themselves, rather than for financial gain or political objectives.

Similarly, the case of Jonathan James, a teenage hacker from the United States, illustrates this motivation. At just 15 years old, James infiltrated several government systems, including NASA, stealing source code and causing significant disruption. His actions seemed motivated by the challenge and thrill of hacking rather than monetary rewards.

While these hackers might not always intend serious harm, their actions can have unintended consequences: disrupting services, compromising data, or exposing vulnerabilities that other malicious actors might exploit.

Revenge or Personal Grievances

Not all cyber threats originate externally—sometimes the greatest risks come from insiders motivated by personal grudges or feelings of revenge. Disgruntled employees, former staff or contractors with authorised access can deliberately cause harm to an organisation by leaking sensitive information, sabotaging systems or stealing data.

One of the most infamous cases involved Edward Snowden, a former NSA contractor who leaked vast amounts of classified information, motivated by a personal belief that the public had the right to know about government surveillance programmes. Though his actions sparked worldwide debate on privacy, they also caused significant damage to intelligence operations.

In the corporate sphere, a UK-based case saw a former IT administrator take revenge after being dismissed by deleting critical files and disabling user accounts, resulting in days of downtime and financial loss.

Such incidents highlight the critical importance of internal controls, thorough monitoring and robust offboarding procedures. Regularly reviewing access rights, implementing the principle of least privilege, and monitoring unusual activity can help detect and prevent insider threats before they escalate.

Organisations must balance trust with vigilance, fostering a positive workplace culture while ensuring employees understand the consequences of malicious actions.

Opportunistic or Accidental Hacking

Not all cyberattacks are the result of carefully planned operations. Many stem from opportunistic or accidental hacking, where attackers use automated tools to scan large numbers of systems for common vulnerabilities. These attacks require minimal effort but can still cause significant damage, especially to organisations or individuals with poor basic cyber hygiene.

Automated bots and scripts regularly probe the internet for unpatched software, weak passwords, misconfigured devices, or open ports. Once a vulnerability is found, the attacker may exploit it to gain access, often without a specific target in mind. This “spray and pray” approach relies on volume rather than precision.

For example, the WannaCry ransomware outbreak in 2017 rapidly spread across the globe by exploiting a known Windows vulnerability. Many affected organisations had failed to apply critical patches, making them vulnerable to this widespread, indiscriminate attack.

These types of attacks highlight the importance of fundamental cybersecurity practices: regularly updating software, using strong, unique passwords, enabling multi-factor authentication, and maintaining good network hygiene. Even basic measures can significantly reduce the risk posed by opportunistic attackers.

While opportunistic hacking might lack the sophistication or motive of targeted attacks, its impact can be equally devastating if proper precautions are not taken.

Mixed Motivations

In reality, cybercriminal motivations are often complex and overlapping rather than clear-cut. Many attacks are driven by a combination of factors—financial, political, ideological, or personal—which can make attribution and defence especially challenging.

A common scenario involves financially motivated cybercriminal groups being hired or tolerated by state actors to carry out attacks that serve national interests. These groups operate with relative impunity in exchange for providing offensive cyber capabilities or disruptive services.

For example, the notorious ransomware group REvil (also known as Sodinokibi) has been linked to criminal operations that sometimes intersect with geopolitical objectives. While primarily motivated by profit through ransomware extortion, there are indications that some affiliates have conducted operations aligning with certain state interests or received indirect protection from their home governments.

Such hybrid motivations complicate the threat landscape, blurring the lines between organised crime and state-sponsored espionage or sabotage. For defenders, understanding these intertwined incentives is crucial for developing effective cyber defence strategies and threat intelligence.

Conclusion

Cybercriminals are motivated by a wide and varied range of factors—from financial gain and political agendas to personal grudges and the pursuit of prestige. Understanding these diverse motivations is essential for organisations seeking to build effective defences in an increasingly complex cyber threat landscape.

By recognising what drives threat actors, businesses and individuals can better anticipate potential attack vectors, prioritise security investments, and tailor their incident response strategies accordingly. A threat-informed defence approach goes beyond technical measures, incorporating intelligence, awareness and proactive risk management.

As cyber threats continue to evolve, adopting a comprehensive, informed security posture is no longer optional—it is vital. Organisations should take active steps to understand their adversaries, strengthen their defences, and cultivate a culture of vigilance to stay ahead in the ongoing battle against cybercrime.

Header Photo by Furkan Elveren on Unsplash

"Analysis
Investigation, Opinion

Mastering the Analysis of Competing Hypotheses (ACH): A Practical Framework for Clear Thinking

In an age of information overload, uncertainty, and complex decision-making, clear analytical thinking is more crucial than ever. The Analysis of Competing Hypotheses (ACH) is a structured method designed to cut through ambiguity and support objective, evidence-based conclusions. Originally developed by Richards J. Heuer, Jr., a veteran of the U.S. intelligence community, ACH was created to help analysts systematically evaluate multiple hypotheses without falling prey to cognitive biases and premature conclusions.

At its core, ACH shifts the analytical focus from proving a favoured hypothesis to disproving less likely alternatives, ensuring that conclusions are reached through a process of elimination rather than assumption. This approach is especially valuable in fields where decisions must be made in the face of incomplete or conflicting data, such as intelligence, cybersecurity, business strategy, and investigative research.

In this article, we’ll explore the foundational principles of ACH, guide you through its step-by-step methodology, and illustrate how to apply it in real-world scenarios. Whether you’re an analyst, decision-maker, or simply someone seeking to sharpen your critical thinking skills, this practical framework offers a powerful tool for navigating complexity with clarity and rigour.

What is the Analysis of Competing Hypotheses?

The Analysis of Competing Hypotheses (ACH) is a structured analytical technique that helps individuals and teams evaluate multiple possible explanations for an event, trend, or problem—all at the same time. Rather than focusing on finding evidence that supports a single favoured hypothesis, ACH encourages analysts to test all plausible alternatives and to prioritise disconfirming evidence over confirming data.

This method stands in contrast to traditional analysis, where there is often a tendency to latch onto the most obvious explanation early on and seek only evidence that backs it up. That approach, while intuitive, is prone to cognitive pitfalls such as confirmation bias, groupthink, and premature closure.

By explicitly laying out competing hypotheses and methodically evaluating each against the available evidence, ACH helps to minimise bias, highlight critical assumptions, and improve judgement, particularly in situations that are ambiguous, fast-moving, or laden with incomplete information.

Ultimately, ACH is less about finding the answer and more about narrowing down the field of possibilities through a process that is transparent, reproducible, and intellectually disciplined.

The ACH Process Step-by-Step

The Analysis of Competing Hypotheses is more than just a checklist—it’s a disciplined approach to structuring your thinking, challenging assumptions, and arriving at well-supported conclusions. Below is an expanded walkthrough of the seven core steps, each designed to promote clarity and rigour in decision-making.

1. Define the Question or Problem

A clear, unbiased problem statement is the foundation of effective analysis. This step is about narrowing the scope of inquiry and making sure the question does not contain built-in assumptions.

Tips for framing your question:

  • Avoid language that implies causality or blame
  • Be as specific as the data allows
  • Keep it neutral and open-ended

Example:
 Why did a system failure occur in a secure network?
 This framing encourages investigation without assuming intent, method, or actor.

A poorly worded question—e.g., “Who caused the attack on our network?”—limits thinking prematurely by assuming the event was malicious and externally driven.

2. List All Plausible Hypotheses

The goal here is to generate a comprehensive list of explanations for the issue. It’s critical to suspend judgment and avoid discarding possibilities too early, especially those that feel uncomfortable or less likely at first glance.

Use techniques like brainstorming, consultation with diverse stakeholders, and red teaming to uncover blind spots.

Example Hypotheses:

  • H1: Insider sabotage
  • H2: External cyberattack
  • H3: Configuration error
  • H4: Third-party service failure
  • H5: Power or environmental disruption

Even if some hypotheses seem implausible, including them ensures a more robust analysis, and sometimes the least obvious explanation turns out to be the correct one.

3. Identify Evidence and Arguments

At this stage, you gather all the information that could potentially support or contradict your hypotheses. This includes:

  • Observational data (logs, reports, witness accounts)
  • Technical indicators (malware signatures, access logs)
  • Expert assessments
  • Circumstantial clues

For each piece of evidence, evaluate two things:

  • Source reliability: How trustworthy is the origin (e.g., system logs vs. anonymous tips)?
  • Information credibility: How plausible or accurate is the content?

Also consider whether the evidence is:

  • Direct or indirect
  • Confirmed or unverified
  • Timely or outdated

Pro tip: Avoid cherry-picking. Include evidence that contradicts your initial instincts—this is where real insight often lies.

4. Analyse Consistency

This is the heart of the ACH method: building a matrix that compares each hypothesis against each piece of evidence.

You’ll mark whether each piece of evidence is:

  • Consistent with the hypothesis
  • Inconsistent (i.e., contradicts it)
  • Neutral (i.e., not relevant to that hypothesis)

Example Matrix:

EvidenceH1: Insider sabotageH2: External cyberattackH3: Configuration error
Admin account accessed remotely at 2am✔️ Consistent✔️ Consistent❌ Inconsistent
No malware signatures detected✔️ Consistent❌ Inconsistent➖ Neutral
Recent patch deployed without testing❌ Inconsistent➖ Neutral✔️ Consistent
No third-party access in logs✔️ Consistent❌ Inconsistent✔️ Consistent

This matrix helps you visualise the weight and distribution of evidence, especially in identifying which hypotheses have significant inconsistencies.

5. Refine the Matrix

Now that the matrix is populated, focus on evaluating the diagnostic value of each piece of evidence. Ask yourself:

  • Which pieces most clearly discriminate between hypotheses?
  • Are there patterns that suggest certain hypotheses are clearly weaker?

ACH places particular emphasis on inconsistencies rather than confirmations. A single strong inconsistency can eliminate a hypothesis, while consistent evidence might apply to multiple hypotheses and be less useful in narrowing options.

Refining may also involve revisiting earlier assumptions, adjusting hypotheses, or seeking new evidence to fill gaps.

6. Draw Tentative Conclusions

This is the interpretive phase—based on the refined matrix, identify which hypothesis is least burdened by inconsistent evidence. Remember, this doesn’t mean it has the most supporting evidence, but rather that it stands up better under scrutiny.

Be cautious not to overstate certainty. If multiple hypotheses remain viable, say so. ACH supports probabilistic thinking, not premature conclusions.

Key reminders:

  • Avoid selecting the “most comfortable” hypothesis
  • Document your reasoning and uncertainties
  • Stay open to revision as new evidence emerges

7. Identify Milestones or Indicators

ACH is not static. Situations evolve, and so should your analysis. Define a set of indicators—specific events, behaviours, or pieces of data—that, if observed, would confirm, challenge, or refine your conclusion.

Examples:

  • Discovery of malware indicating a known threat actor (would support H2)
  • Forensic evidence of misconfiguration traced to recent update (would support H3)
  • Repetition of similar failures in unrelated systems (might suggest a broader issue)

Establish a plan for ongoing monitoring. This step ensures your conclusions remain grounded in reality as the situation unfolds and prevents analytical drift over time.


Analysis of Competing Hypotheses

Practical Example: ACH in Action

To demonstrate the practical value of the Analysis of Competing Hypotheses, let’s walk through a realistic scenario involving a suspected cybersecurity incident at a mid-sized financial services firm. This example illustrates each step of the ACH process in context, showing how structured analysis can lead to clearer conclusions—even in the face of ambiguity.

Scenario: Unexpected System Downtime in a Secure Network

Background:
At 03:15 on a Tuesday morning, the firm’s primary transaction server went offline, causing a six-hour disruption to client services. The network is normally robust and protected by multiple layers of defence. Internal monitoring systems flagged the event, but initial diagnostics were inconclusive.

The CTO initiates an ACH analysis to determine what caused the failure.

Step 1: Define the Question or Problem

The team agrees to frame the central question as:

What is the most plausible explanation for the unexpected system outage on the secure transaction server?

This wording avoids assumptions about cause or intent and invites multiple lines of inquiry.

Step 2: List All Plausible Hypotheses

The team brainstorms and agrees on the following hypotheses:

  • H1: External cyberattack (e.g., malware, DDoS)
  • H2: Insider sabotage (malicious insider or misuse)
  • H3: Configuration or patching error
  • H4: Hardware failure or infrastructure fault
  • H5: Scheduled maintenance error or oversight

The list is deliberately inclusive to prevent tunnel vision.

Step 3: Identify Evidence and Arguments

The team compiles evidence from logs, interviews, monitoring tools, and server diagnostics. Notable pieces of evidence include:

  • E1: Server logs show a reboot command issued remotely at 03:14
  • E2: No malware signatures or IOCs (Indicators of Compromise) detected
  • E3: A new patch was installed the day prior without full regression testing
  • E4: No external traffic spikes or anomalies around the time of the incident
  • E5: Access logs show a junior administrator logged in remotely at 03:12
  • E6: Server hardware passed all post-incident diagnostics
  • E7: Change management calendar incorrectly listed maintenance for the wrong server

Each item is tagged with a confidence rating and source reliability to support judgment later.

Step 4: Analyse Consistency

The team creates a matrix to compare each hypothesis against the evidence.

EvidenceH1: CyberattackH2: Insider SabotageH3: Config ErrorH4: Hardware FaultH5: Maintenance Error
E1: Remote reboot at 03:14✔️ Consistent✔️ Consistent✔️ Consistent➖ Neutral✔️ Consistent
E2: No malware or IOCs found❌ Inconsistent✔️ Consistent➖ Neutral➖ Neutral➖ Neutral
E3: Patch installed the day before➖ Neutral➖ Neutral✔️ Consistent➖ Neutral➖ Neutral
E4: No external anomalies❌ Inconsistent➖ Neutral➖ Neutral➖ Neutral➖ Neutral
E5: Junior admin logged in remotely➖ Neutral✔️ Consistent✔️ Consistent➖ Neutral❌ Inconsistent
E6: Hardware passed diagnostics➖ Neutral➖ Neutral➖ Neutral❌ Inconsistent➖ Neutral
E7: Calendar showed the wrong server➖ Neutral➖ Neutral➖ Neutral➖ Neutral✔️ Consistent

Step 5: Refine the Matrix

Focusing on disproving hypotheses, the team notes:

  • H1 (Cyberattack) has two clear inconsistencies (E2 and E4)
  • H4 (Hardware fault) is contradicted by E6
  • H5 (Maintenance error) is weakened by E5, as the admin wasn’t scheduled to access that system

H2 (Insider sabotage) and H3 (Configuration error) remain more viable. The presence of an unscheduled login and recent patching suggests a blend of human and technical causes.

The most diagnostic evidence appears to be E2 (no malware) and E3 (untested patch), which significantly affect H1 and H3, respectively.

Step 6: Draw Tentative Conclusions

H1 (Cyberattack) and H4 (Hardware fault) are largely ruled out.
H5 (Maintenance error) is possible but lacks strong support and includes an inconsistency.
That leaves:

  • H2 (Insider sabotage): Plausible, especially with unexpected admin access
  • H3 (Configuration error): Strongly supported by evidence, with few inconsistencies

Given that the administrator may have unknowingly pushed a faulty patch, H3 is deemed the most probable hypothesis, with H2 remaining a secondary consideration requiring HR review.

Step 7: Identify Milestones or Indicators

To confirm or disprove the working conclusion, the team outlines the following future indicators:

  • Confirmation of the patch’s fault during follow-up testing (would support H3)
  • HR interview with the admin reveals intent or confusion (could support or refute H2)
  • Any signs of privilege misuse or unusual access patterns (would raise concern for H2)
  • Vendor advisory on the patch’s known issues (further supporting H3)

The analysis will be updated once these indicators are assessed. In the meantime, patching procedures are temporarily suspended, and access controls are reviewed.


Final Conclusion

The structured application of ACH helped the team reach a reasoned, defensible conclusion while keeping alternate hypotheses in play. Rather than jumping to the common assumption of a cyberattack, the analysis revealed a more mundane but equally critical root cause: likely misconfiguration following a poorly tested software update.

Real-World Reference: The Lucy Letby Case

The power of ACH is underscored by its implicit use in high-stakes investigations such as the Lucy Letby trial. Prosecutors highlighted that Letby was the only staff member present during every critical incident involving infant patients—a fact established through careful analysis of shift patterns and timelines. By systematically evaluating competing hypotheses about who could have caused harm, investigators effectively used the same logic underpinning ACH: disproving alternative explanations and focusing on the hypothesis best supported by consistent evidence. This approach helped build a compelling, structured case based on opportunity and timing, demonstrating ACH’s practical application beyond intelligence into criminal justice.

Benefits and Limitations of ACH

The Analysis of Competing Hypotheses (ACH) offers a powerful framework for navigating complex, ambiguous, or high-stakes problems. But like any method, it comes with both strengths and limitations. Understanding these helps practitioners apply it effectively and appropriately.

Benefits of ACH

1. Reduces Cognitive Bias
ACH is specifically designed to counteract common mental pitfalls, such as confirmation bias and premature conclusions. By forcing the analyst to evaluate all plausible hypotheses and focus on disconfirming evidence, it encourages objectivity and balance.

2. Encourages Structured Thinking
Rather than relying on intuition or fragmented information, ACH imposes a disciplined approach. Analysts must document each step, weigh evidence methodically, and justify conclusions. This structure makes reasoning transparent and defensible, especially important in intelligence, law enforcement, or regulatory settings.

3. Handles Ambiguity and Complexity Well
ACH is particularly effective when information is incomplete, uncertain, or contradictory. By assessing how each piece of evidence aligns (or doesn’t) with multiple hypotheses, it accommodates complexity without oversimplifying.

4. Improves Group Collaboration and Debate
In team settings, ACH helps avoid groupthink by providing a common analytical language and framework. It gives structure to collaborative analysis, enabling different perspectives to be tested against the same evidence matrix.

5. Highlights Gaps and Guides Collection
The process often reveals where evidence is weak or missing, helping analysts identify what further data needs to be gathered. Diagnostic indicators can also be flagged for future monitoring.


Limitations of ACH

1. Time-Consuming
ACH is not always suited to fast-moving or reactive situations. Building and refining matrices, especially for complex cases with numerous hypotheses, can be labour-intensive.

2. Dependent on Quality of Input
The effectiveness of ACH depends entirely on the quality and reliability of the evidence fed into it. Incomplete, misleading, or low-confidence data can skew conclusions, even if the process itself is rigorous.

3. May Oversimplify Nuance
Although ACH structures thinking, it can sometimes encourage a binary view of evidence (e.g. consistent/inconsistent/neutral). This may not capture subtleties, degrees of relevance, or contextual complexity unless analysts make an effort to interpret carefully.

4. Requires Analytical Discipline
The method assumes a willingness to challenge assumptions, avoid premature closure, and remain open to changing conclusions as new evidence arises. In practice, this intellectual discipline can be hard to maintain, especially under pressure.

5. Not a Substitute for Domain Expertise
ACH supports analysis, but it does not replace subject matter knowledge. Without expert insight to interpret evidence correctly, even a well-constructed ACH matrix can produce flawed conclusions.


ACH is a powerful complement to critical thinking, not a magic solution. Used thoughtfully, it strengthens the quality of judgment and provides a clear audit trail for how conclusions were reached.

Tools and Resources

While the Analysis of Competing Hypotheses (ACH) can be applied using simple pen-and-paper methods, various tools can help structure the process, especially when working with complex datasets or collaborating with others. Below are some practical tools that support ACH-style analysis.

Manual Tools

Spreadsheets (e.g., Excel, Google Sheets)
Spreadsheets remain a reliable and widely used method for building ACH matrices. Users can list hypotheses across the top, evidence down the side, and use consistent symbols or colour codes to mark whether each item of evidence is consistent, inconsistent, or neutral. This method offers full transparency and is easily adaptable for individual or team use.

Printable ACH Templates
Basic ACH grids are available as printable templates and can be useful in workshops, briefings, or offline environments. These encourage clarity of thought without requiring technical platforms.

Digital Tools

PARC ACH Tool
Developed by the Palo Alto Research Center, this free, downloadable tool guides users through the ACH process, including hypothesis generation, evidence scoring, matrix creation, and conclusion development. It’s well-suited for training and operational use.

IBM i2 Analyst’s Notebook
Though not purpose-built for ACH, Analyst’s Notebook allows for sophisticated mapping of relationships between people, events, and data, which can support structured hypothesis testing in investigative contexts.


Recommended Reading

  • Psychology of Intelligence Analysis – Richards J. Heuer Jr.
    The original source text on ACH offers both theory and practical examples. Essential reading for analysts across sectors.
  • Tradecraft Primer: Structured Analytic Techniques for Intelligence Analysis – CIA (declassified)
    A practical manual outlining ACH alongside other structured methods such as key assumptions checks and red teaming. Freely available online.

Conclusion

In a world increasingly defined by uncertainty, complexity, and competing narratives, the Analysis of Competing Hypotheses (ACH) offers a methodical way to cut through ambiguity. Originally developed for intelligence professionals, its value extends far beyond, offering anyone engaged in investigative work, cybersecurity, risk assessment, or strategic decision-making a practical framework for clearer thinking.

By focusing on disproving rather than confirming, ACH helps analysts avoid cognitive traps and build conclusions on firmer ground. It doesn’t guarantee certainty, but it does promote discipline, transparency, and intellectual honesty — qualities that are increasingly vital in high-stakes environments.

While the process may require time and rigour, the payoff is well-structured, defensible conclusions. Whether you’re a security analyst examining network breaches, a business leader weighing strategic options, or a researcher interpreting complex data, ACH provides a repeatable model for navigating complexity with confidence.

Incorporating ACH into your analytical toolkit is more than a method — it’s a mindset shift towards structured scepticism, clarity of thought, and resilient decision-making. The more widely it’s adopted, the stronger our collective reasoning becomes.

Header photo by Milad Fakurian on Unsplash.

Photo by fabio on Unsplash.

"Understanding
Investigation, Opinion

Understanding SCATTERED SPIDER: Tactics, Targets, and Defence Strategies

In recent months, a wave of disruptive cyberattacks has swept across high-profile organisations in both the UK and the US, affecting sectors ranging from hospitality and telecommunications to finance and retail. Many of these incidents share a common thread: attribution to a threat actor known as SCATTERED SPIDER, a group now gaining notoriety for its aggressive use of social engineering and its partnership with the DragonForce ransomware-as-a-service (RaaS) operation.

Unlike traditional ransomware gangs that rely heavily on technical exploits or brute-force tactics, SCATTERED SPIDER stands out for its deeply manipulative approach. The group has repeatedly demonstrated its ability to impersonate employees, deceive IT support teams, and bypass multi-factor authentication (MFA) through cunning psychological tactics. Often described as “native English speakers,” they are suspected to operate in or have ties to Western countries, bringing a cultural fluency that makes their phishing and phone-based attacks alarmingly effective.

As law enforcement and cybersecurity professionals scramble to contain the fallout from recent attacks, one thing is clear: SCATTERED SPIDER is not just another ransomware affiliate. They represent a shift toward human-centric intrusion strategies, blending technical skill with social deception in a way that challenges even well-defended organisations.

This article takes a closer look at how SCATTERED SPIDER operates, the tools they use, including DragonForce RaaS and, most importantly, what practical steps individuals and organisations can take to reduce their exposure to this growing threat.

Image Credit: Crowdstrike

Who Is SCATTERED SPIDER?

SCATTERED SPIDER is the name given to a loosely affiliated cybercriminal group that has quickly gained attention for its highly targeted and persistent campaigns against major organisations. Believed to be active since at least 2022, the group is often classified as an Initial Access Broker (IAB) and affiliate actor, working both independently and in partnership with larger ransomware collectives, most notably the ALPHV/BlackCat operation.

What sets SCATTERED SPIDER apart is not just its technical acumen, but its expert use of social engineering, often executed in fluent English and with a level of cultural familiarity that suggests the group is likely based in or has strong ties to the US or UK. Unlike many ransomware actors operating out of Eastern Europe or Russia, SCATTERED SPIDER’s tactics are tailored to Western corporate environments, allowing them to convincingly impersonate staff, manipulate helpdesk personnel, and bypass traditional security barriers with unnerving ease.

The group’s motivation is primarily financial, but their techniques are unusually aggressive. Rather than simply deploying ransomware after gaining access, SCATTERED SPIDER takes the time to navigate internal systems, escalate privileges, and exfiltrate data, ensuring maximum impact and leverage during extortion. This has included threats to publicly leak sensitive data if ransoms aren’t paid, a tactic made easier by their ties to DragonForce RaaS, a ransomware service that offers data leak platforms and other tools to affiliates.

Notable incidents attributed to SCATTERED SPIDER include:

  • The 2023 attack on MGM Resorts, which saw large-scale IT disruption across casinos and hotels in the US, was reportedly caused by a simple phone-based social engineering ploy.
  • Intrusions into telecommunications and managed service providers, where they have targeted identity infrastructure such as Okta and Active Directory to pivot across networks.
  • Disruption and data theft in the financial and insurance sectors, where highly sensitive customer and operational data were exfiltrated and held to ransom.

These campaigns reveal a group that is not only technically capable but strategically manipulative, leveraging trust, urgency, and insider knowledge to achieve access that many automated tools would struggle to obtain.

The Tools of the Trade: DragonForce RaaS

One of the key enablers of SCATTERED SPIDER’s recent success has been their alignment with DragonForce, a relatively new entrant in the expanding Ransomware-as-a-Service (RaaS) ecosystem. RaaS models have radically altered the cybercrime landscape. Much like SaaS (Software-as-a-Service) in the legitimate tech world, RaaS lowers the barrier to entry for less technically capable threat actors by offering turnkey ransomware toolkits, user-friendly dashboards, and profit-sharing agreements between developers and affiliates.

What Is DragonForce?

DragonForce is a commercially operated ransomware platform, complete with a slick user interface, customer “support” channels, and marketing-style updates promoting new features and obfuscation techniques. While it may not yet have the brand recognition of LockBit or BlackCat, it is gaining traction among cybercriminal groups for its reliability, speed, and aggressive encryption routines.

Its offerings typically include:

  • Highly customisable payloads: Affiliates like SCATTERED SPIDER can tweak encryption settings, file extensions, and ransom notes to suit their targets.
  • Data exfiltration modules: These facilitate double extortion, where files are stolen before encryption and used as additional leverage during ransom negotiations.
  • Dark Web leak portals: Victim data is published or threatened with publication unless payment is made.
  • Access to a central control panel: Affiliates can monitor infected machines, initiate encryption manually, and track ransom payments via cryptocurrency wallets.

These features allow threat actors to operate more like cybercrime startups than ad-hoc hacking collectives.

Why SCATTERED SPIDER Uses DragonForce

SCATTERED SPIDER’s strength lies in gaining initial access, often via phone-based social engineering or SIM-swapping tactics, rather than building their own ransomware from scratch. By outsourcing encryption and extortion capabilities to a RaaS provider like DragonForce, they focus on what they do best: manipulating people, navigating corporate networks, and extracting sensitive data.

In this partnership, DragonForce gains a capable affiliate who can deliver high-value access, and SCATTERED SPIDER gains a ready-made suite of tools to monetise their intrusions. This division of labour reflects a broader shift in cybercrime, one where specialisation and scalability are the name of the game.

DragonForce and the RaaS Economy

It’s important to understand that DragonForce is not an isolated actor. It is part of a wider criminal ecosystem where:

  • Access brokers sell stolen credentials or remote access.
  • Malware developers lease out payloads to trusted affiliates.
  • Negotiators and money launderers offer “aftercare” services.

This ecosystem enables threat actors to operate like businesses, complete with hierarchical roles, profit-sharing models, and even internal dispute resolution mechanisms. In this context, SCATTERED SPIDER is not just a lone wolf but a well-placed operator within a highly coordinated cybercrime supply chain.

Why This Matters

The use of DragonForce by SCATTERED SPIDER highlights two alarming trends:

  1. Professionalisation of ransomware: You no longer need deep technical knowledge to execute devastating attacks; just access, confidence, and a few phone calls.
  2. Faster time-to-impact: With everything from encryption to extortion automated and streamlined, the time between compromise and ransom demand is shrinking rapidly, leaving organisations with little time to detect and respond.

As DragonForce continues to evolve and attract new affiliates, we are likely to see more actors adopt this model of rapid-access, rapid-extortion ransomware operations.

Image Credit: Kaspersky

Anatomy of an Attack: How SCATTERED SPIDER Operates

Understanding how SCATTERED SPIDER executes its attacks is crucial for organisations looking to strengthen their defences. Unlike many ransomware operators who rely on brute-force tactics or mass phishing campaigns, SCATTERED SPIDER favours precision, patience, and psychological manipulation.

Here’s a typical flow of operations observed in their campaigns:

1. Reconnaissance and Target Selection

The group begins by identifying high-value targets, often large enterprises in sectors such as telecommunications, financial services, and IT. They may purchase access to credentials or endpoint telemetry from Initial Access Brokers (IABs) or scrape publicly available information from LinkedIn, press releases, and social media to build detailed profiles of staff and infrastructure.

What makes this phase effective:

  • Use of OSINT to identify staff names, departments, and third-party vendors.
  • Focus on companies with complex IT environments and high tolerance for operational risk—prime candidates for extortion.

2. Initial Access via Social Engineering

Once they’ve identified the right entry point, SCATTERED SPIDER often deploys vishing (voice phishing) or phishing techniques to impersonate internal staff. In some cases, they call help desks pretending to be employees locked out of their accounts, requesting MFA resets or password changes.

This is where their native English and cultural familiarity give them a dangerous edge; they sound credible, confident, and urgent.

Common tactics:

  • Impersonating IT staff or executives to pressure support teams.
  • SIM-swapping or MFA fatigue attacks to intercept or bypass two-factor authentication.
  • Spoofed email domains or compromised inboxes used for internal-style phishing.

3. Credential Harvesting and Privilege Escalation

Once inside, the group moves quickly to extract further credentials. Tools such as Mimikatz, Cobalt Strike, and legitimate Windows administration tools (e.g. PowerShell, PsExec) are used to escalate privileges and move laterally across the network.

They specifically look for access to:

  • Identity infrastructure (Active Directory, Okta, Azure AD)
  • Remote access tools (VPNs, RDP gateways, Citrix)
  • Data repositories containing sensitive customer or business data

This phase may last hours or days, depending on the target’s size and the level of access achieved.

4. Data Exfiltration and Pre-Ransom Preparation

Before deploying ransomware, SCATTERED SPIDER usually exfiltrates a trove of sensitive data. This forms the basis of their double extortion strategy; even if a victim can restore from backups, they may still pay to prevent the public release of confidential files.

Common methods:

  • Compressing and uploading files to cloud storage services or attacker-controlled servers
  • Encrypting and staging data to avoid detection by DLP or antivirus tools

In some cases, the group leaves behind backdoors or admin accounts to retain long-term access or re-extort victims in the future.

5. Ransomware Deployment via DragonForce

Once exfiltration is complete and the environment is primed, SCATTERED SPIDER deploys DragonForce ransomware across the compromised network. The ransomware is configured to encrypt files rapidly and disrupt operations, sometimes including domain controllers and backup servers, to maximise impact.

Victims then receive a ransom note directing them to a Tor-based portal for negotiations. If payment isn’t made within a specified timeframe, stolen data is posted on a leak site associated with DragonForce.


Key Takeaways:

  • SCATTERED SPIDER relies on human error as much as technical vulnerabilities.
  • The group’s knowledge of Western IT environments makes it easier for them to blend in and manipulate systems and staff.
  • Their multi-stage attack chain: access, escalation, exfiltration, encryption, is methodical and difficult to detect in real time.

Image Credit – Reeds Solicitors

Why SCATTERED SPIDER’s Approach Is Especially Dangerous

SCATTERED SPIDER doesn’t operate like a traditional ransomware crew. Their campaigns combine social engineering finesse with technical aggression, resulting in a hybrid threat model that blends cybercrime with tactics more often associated with espionage groups. Here’s why they stand out and why they’re so difficult to defend against.

1. Deep Impersonation and Real-Time Manipulation

Unlike typical phishing groups that rely on mass email blasts, SCATTERED SPIDER employs live, targeted deception. Their operators speak fluent, unaccented English and are adept at impersonating IT personnel, executives, or employees in distress.

They frequently call help desks or IT support lines, using:

  • Personalised information gathered through OSINT
  • Spoofed phone numbers and internal-sounding email addresses
  • Calm, confident delivery to manipulate support staff in real time

This level of human-centred deception is rarely seen in conventional cybercrime campaigns and poses a serious challenge for security teams.

2. Precision Targeting of Identity Infrastructure

SCATTERED SPIDER understands that identity is the new perimeter. Rather than merely compromising a system, they aim to take control of identity and access management tools like:

  • Okta
  • Active Directory
  • Azure AD
  • SSO and MFA services

By doing so, they’re not just accessing individual endpoints, they’re taking over the core trust fabric of the organisation. Once they own your identity systems, lateral movement and persistence become trivially easy.

3. Speed and Aggression Outpacing Detection

While many attackers spend weeks in a network quietly collecting data, SCATTERED SPIDER moves with urgency and intent. In many cases:

  • Initial access to ransomware deployment can take place in less than 48 hours.
  • They bypass traditional controls using legitimate tools (Living off the Land), leaving minimal forensic traces.
  • They often disable security tools, delete logs, or backdoor admin accounts to stay one step ahead.

Traditional defences based on known signatures, blacklists, or passive monitoring are often too slow or too blind to respond in time.

4. Blurring the Line Between Cybercrime and Nation-State Tactics

Although motivated by financial gain rather than geopolitics, SCATTERED SPIDER’s tradecraft exhibits a level of maturity and adaptation more typical of state-sponsored APT groups. This includes:

  • Tailored intrusion techniques for specific industries and environments
  • Multi-stage attacks with operational patience
  • Use of multiple extortion channels, including PR pressure and data leak sites

This hybrid operational model: part ransomware gang, part APT, means traditional classifications don’t fully capture the scope of their threat. For defenders, this creates both strategic confusion and escalating risk.

In short, SCATTERED SPIDER is dangerous not just because of what they do, but how they do it. Their blend of psychological manipulation, identity compromise, and rapid escalation makes them one of the most formidable threats facing organisations today.

Defending Against SCATTERED SPIDER: Practical Guidance

While SCATTERED SPIDER’s tactics are sophisticated, they often exploit basic lapses in process, communication, and identity management. That means there are precautions organisations can take to harden themselves against this type of threat, without needing to reinvent their entire security stack.

1. Reinforce Help Desk Security Protocols

Since SCATTERED SPIDER frequently targets help desks and support teams, ensure those teams are trained to:

  • Never reset MFA or passwords without high-assurance identity verification.
  • Use call-back procedures or out-of-band verification for unusual requests.
  • Flag repeated or urgent requests as potential social engineering.

Adding simple checklists and mandatory escalation paths for sensitive account changes can drastically reduce social engineering success rates.

2. Harden Identity and Access Management

Identity remains a prime attack surface. To reduce risk:

  • Enforce phishing-resistant MFA, such as hardware tokens or app-based push authentication with device binding (rather than SMS or email codes).
  • Implement just-in-time access and least privilege policies for administrative accounts.
  • Regularly audit inactive accounts, especially third-party vendors and former employees.

Integrate identity telemetry into your detection stack: suspicious logins, MFA resets, or logins from new devices should trigger alerts.

3. Monitor for Signs of Lateral Movement

Once SCATTERED SPIDER is inside a network, time is of the essence. Deploy tools and strategies to detect:

  • Unusual use of remote admin tools (e.g. PowerShell, PsExec)
  • Use of credential dumping tools or abnormal privilege escalation
  • Lateral movement attempts, especially to identity infrastructure like Active Directory or Okta

EDR/XDR platforms with good behavioural analytics can be critical here, especially when coupled with 24/7 monitoring or MDR services.

4. Protect Your Data, and Know Where It Is

Given the group’s focus on data theft prior to encryption, prevention isn’t just about backups:

  • Map your critical data locations, especially customer, financial, and IP-related data.
  • Use Data Loss Prevention (DLP) tools to monitor exfiltration patterns.
  • Segment sensitive environments and restrict data access to only those who need it.

Ensure that backups are not just secure and segmented from your main network, but also tested regularly.

5. Prepare for the Human Side of a Crisis

Even strong technical controls can be undone by panic or poor decision-making in the moment. Prepare:

  • A ransomware playbook with clear response roles, legal guidance, and communications plans.
  • Crisis simulations or tabletop exercises that include scenarios involving data leaks and public extortion.
  • Training for executives and PR teams on how to manage the reputational and regulatory impact.

Remember: SCATTERED SPIDER succeeds by catching organisations off guard, so make sure your teams know exactly how to respond under pressure.


Security Culture Is Your Best Defence

At the end of the day, SCATTERED SPIDER’s tactics work because they exploit human trust, urgency, and complexity. Investing in detection tools is important, but fostering a culture of scepticism, verification, and shared responsibility across the organisation is what truly builds resilience.

Stay Vigilant, Stay Informed

SCATTERED SPIDER has proven that ransomware is no longer just about encrypted files and ransom notes — it’s about controlling identities, deceiving people, and outpacing traditional defences. Their campaigns demonstrate just how effective a threat actor can be when they combine technical proficiency with social engineering and real-time manipulation.

What makes them especially dangerous is not just the tools they use, but the tactics and mindset behind their operations. This is a group that studies its targets, adapts rapidly, and blends psychological and technical attacks with striking efficiency.

For organisations in the UK, the US, and beyond, the message is clear: security isn’t just a technology problem — it’s a people and process problem too. Preventing the next SCATTERED SPIDER-style breach means:

  • Educating and empowering support staff
  • Hardening identity infrastructure
  • Monitoring for the unexpected
  • And rehearsing how you’ll respond under pressure

Cybercriminals evolve constantly. So must we.

Header image > Photo by Егор Камелев on Unsplash.

"Seeing
Opinion, OSINT

Seeing Clearly: Understanding and Addressing Bias in OSINT

Open-source intelligence (OSINT) has become an essential part of modern investigations, threat analysis, and decision-making. By leveraging publicly available information from the surface web, social media, forums, and more obscure corners of the internet, OSINT practitioners can uncover insights without the need for intrusive or covert methods. But as with any form of intelligence gathering, the process is far from objective.

Bias — whether introduced by the analyst, the tools used, or the sources themselves — can significantly distort findings. In an age where data is vast, varied, and often unverified, understanding and mitigating bias is not just good practice, it’s a necessity.

In this blog post, we’ll explore the different types of bias that can affect OSINT, from unconscious assumptions to platform-driven distortions. We’ll also look at the real-world consequences of unchecked bias and offer practical steps to help analysts and organisations reduce its impact. Because when it comes to intelligence, clarity and objectivity are key — and bias is the silent threat that clouds both.

What Is Bias in OSINT?

Bias, in the context of OSINT, refers to any distortion or influence that affects how information is collected, interpreted, or presented. It can arise from a wide range of sources — the tools we use, the platforms we search, the assumptions we bring with us, and even the way we frame our intelligence requirements.

Importantly, bias isn’t always intentional. Much of it operates at a subconscious level, shaped by cultural norms, past experiences, professional habits, or institutional practices. And in OSINT — where we often deal with vast, unstructured, and fast-moving data — even small biases can significantly skew the outcome of an investigation.

Bias can enter the OSINT process at every stage. It may influence the types of sources we prioritise, the way we interpret ambiguous content, or the confidence we place in particular findings. Analysts may unconsciously favour information that supports a working theory, or dismiss data that doesn’t align with an expected narrative. Meanwhile, digital tools and search algorithms can subtly reinforce these patterns, feeding analysts what they’re likely to click on — not necessarily what’s most accurate or relevant.

Recognising the presence of bias is the first step in mitigating its effects. In the sections that follow, we’ll explore some of the most common types of bias in OSINT work, and how they can impact the quality and reliability of our intelligence.

Types of Bias Common in OSINT

Bias in OSINT can creep in at any stage of the intelligence lifecycle — from the moment an analyst frames a question, to the sources they choose, the tools they rely on, and how they interpret the information gathered. These biases, often unconscious, can impact the reliability, relevance, and objectivity of an intelligence product. Below are the most prevalent types of bias that OSINT practitioners should be aware of, alongside examples and mitigation tips.

Selection Bias

Selection bias arises when the information an analyst collects is not representative of the broader landscape, often because certain types of sources or platforms are favoured over others. This can be due to habit, language familiarity, ease of access, or time constraints.

Example:
An analyst researching political disinformation may rely heavily on Twitter data, missing coordinated narratives being pushed on Telegram or region-specific platforms like VK or Weibo.

Why it matters:
If the selected sources don’t reflect the full spectrum of available information, the resulting intelligence may be incomplete, misleading, or skewed towards a particular narrative or demographic.

How to reduce it:

  • Use a diverse set of platforms and media types (forums, blogs, videos, alt-tech sites).
  • Include regional and language-specific sources wherever possible.
  • Revisit and regularly reassess your go-to sources to prevent over-reliance.

Confirmation Bias

Confirmation bias is the tendency to look for or interpret information in a way that supports an existing hypothesis or belief, while disregarding evidence that contradicts it. This is especially common when an analyst is under pressure to produce a “smoking gun” or validate a stakeholder’s expectations.

Example:
While investigating a suspected nation-state actor, an analyst focuses exclusively on TTPs (Tactics, Techniques, and Procedures) associated with that actor, ignoring signs that the activity could point to a different group or a false flag operation.

Why it matters:
Confirmation bias can lead to poor attribution, misinformed decisions, or ineffective mitigation strategies. It also limits an analyst’s ability to explore alternative hypotheses.

How to reduce it:

  • Apply structured analytic techniques such as the Analysis of Competing Hypotheses (ACH).
  • Collaborate with other analysts to test assumptions and encourage critical challenge.
  • Document reasoning and acknowledge uncertainty in assessments.

Language and Cultural Bias

Language barriers and cultural unfamiliarity can affect how information is gathered and interpreted. Analysts working in a second language — or relying on machine translation — may misread tone, sarcasm, or idiomatic expressions. Cultural norms can also impact how certain behaviours are perceived.

Example:
An English-speaking analyst may misinterpret the tone of Arabic-language social media posts due to literal translation, mistaking satire or frustration for calls to violence.

Why it matters:
Poor interpretation can lead to false positives, mischaracterisation of intent, or overlooking local context. This is particularly critical in geopolitical, extremist, or criminal investigations.

How to reduce it:

  • Use native speakers or trusted translation partners when possible.
  • Consult regional experts for cultural insight.
  • Avoid making assumptions based solely on automated translations or surface-level interpretations.

Tool and Platform Bias

The tools we use to collect and analyse data are not neutral. Search engines, social media platforms, and scraping tools all apply filters, ranking algorithms, and personalisation — often without the user’s awareness. This can prioritise certain types of content and bury others, skewing the analyst’s perception of what is prevalent or important.

Example:
Google search results vary depending on location, search history, and user profile. An analyst may believe a narrative is trending globally when in fact it’s only prominent in their localised feed.

Why it matters:
Platform bias can lead to a false sense of consensus or popularity. It also risks amplifying certain voices while suppressing dissenting ones.

How to reduce it:

  • Use multiple search engines and anonymised browsers (e.g. Tor or VPNs).
  • Test queries in incognito/private browsing modes.
  • Be aware of default settings in commercial tools — understand what’s being excluded or prioritised.

Data Availability Bias

Data availability bias refers to the over-reliance on information that is easiest to find, most recent, or most abundant. Analysts may gravitate towards high-volume data sources (like Reddit or Twitter) because they are continuously updated and easy to search — at the expense of smaller, less visible sources that may be more valuable.

Example:
An OSINT report on cybercriminal activity may cite dozens of tweets and blog posts but fail to include key discussions taking place in closed forums or encrypted messaging groups.

Why it matters:
The quantity of available data doesn’t always equate to quality or relevance. Prioritising what’s visible over what’s essential can distort the intelligence picture and give a false sense of completeness.

How to reduce it:

  • Establish clear intelligence requirements before collection begins.
  • Allocate time to seek out hard-to-find or niche sources.
  • Treat gaps in data as a signal — not just an absence.

Together, these biases form a web of influence that can compromise even the most well-intentioned investigations. 


Real-World Consequences of Bias in OSINT

Bias in OSINT isn’t just a theoretical concern — it has real-world implications. When unchecked, bias can lead to flawed assessments, damaged reputations, operational missteps, and even legal or ethical breaches. Whether you’re conducting corporate investigations, monitoring geopolitical events, or assessing cyber threats, the integrity of your findings depends on how rigorously you confront bias throughout the process.

Here are some key consequences of biased OSINT!

Flawed Decision-Making

Biased intelligence can feed directly into poor decisions, especially in fast-moving environments where leadership relies heavily on OSINT to shape strategy or response.

Example:
A security team monitoring social unrest misinterprets online sentiment due to over-reliance on English-language Twitter data. As a result, they misjudge the timing and location of protest activity, leading to inadequate resource allocation and reputational damage for the organisation.

Impact:
Misinformed decisions can result in financial losses, safety risks, or missed opportunities to intervene early in an emerging threat.

Inaccurate Attribution and Threat Profiling

In cyber threat intelligence, OSINT is often used to support attribution — linking incidents to actors or groups. Bias in source selection or interpretation can lead to false conclusions about who is behind an attack or what their motives might be.

Example:
An analyst attributes a phishing campaign to a well-known ransomware gang based on superficial similarities to a past incident, without exploring the possibility of copycat tactics. Later evidence reveals the activity was the work of a different actor altogether.

Impact:
Faulty attribution may lead to targeting the wrong group, damaging diplomatic relationships, or overlooking the true threat actor.

Overlooking Emerging Threats

Bias towards mainstream or high-visibility platforms can cause analysts to miss activity in fringe spaces where new narratives or tactics often emerge first.

Example:
While monitoring disinformation around an election, analysts focus on Facebook and YouTube but fail to detect early mobilisation efforts on fringe platforms like 4chan or niche messaging channels.

Impact:
Failure to detect early-stage planning or sentiment shifts can delay mitigation efforts and allow threats to escalate unchecked.

Offensive cyber threat intelligence

Reputational and Legal Risks

If an organisation bases public statements or internal actions on flawed OSINT, it could face reputational fallout — or worse, legal consequences.

Example:
A company issues a threat advisory naming a suspected actor based on an OSINT report later revealed to be based on misinterpreted data. The accused actor contests the findings publicly, leading to reputational damage and potential liability.

Impact:
Poorly substantiated claims can erode trust in your organisation’s intelligence capabilities and create significant legal exposure.

OSINT SOS Intelligence

Analyst Burnout and Operational Inefficiency

Constantly chasing data that confirms a pre-existing view can lead to tunnel vision and missed insight. It also increases cognitive load, as analysts struggle to reconcile contradictory findings with an inflexible narrative.

Example:
An intelligence team spends weeks reinforcing an incorrect assumption because early findings were never challenged. Late-stage doubts lead to rework and missed deadlines.

Impact:
Bias drains time, undermines analyst confidence, and reduces the overall efficiency of the OSINT process.

By understanding and acknowledging these consequences, OSINT professionals can treat bias not just as a theoretical flaw but as a practical risk — one that can and should be actively mitigated. In the next section, we’ll explore how to do exactly that: recognising bias in your own process, and adopting safeguards to reduce its impact.


How to Identify and Mitigate Bias in OSINT Investigations

Tackling bias in OSINT isn’t about eliminating it entirely — that’s virtually impossible. Instead, the goal is to recognise where bias may creep in, actively question your assumptions, and build safeguards into your processes to keep your intelligence as accurate, balanced, and reliable as possible. Below are key strategies for identifying and mitigating bias throughout the OSINT lifecycle.

Develop Self-Awareness and Encourage Critical Thinking

Awareness is the first step. Bias is often unconscious, so analysts must learn to reflect on their own thought processes and remain open to challenge.

Tips:

  • Ask yourself: “What assumptions am I making here?”
  • Encourage peer review within your team — a second set of eyes can catch blind spots you might miss.
  • Maintain a mindset of curiosity over certainty. Avoid becoming too attached to an early hypothesis.

Use Structured Analytic Techniques (SATs)

Structured Analytic Techniques are proven tools to help analysts explore alternative explanations, test assumptions, and reduce cognitive traps.

Recommended techniques:

  • Analysis of Competing Hypotheses (ACH): List all possible explanations and evaluate evidence for and against each.
  • Red Teaming: Have a colleague deliberately challenge your assumptions and present counter-arguments.
  • Devil’s Advocacy: Take an opposing viewpoint to test the strength of your conclusions.

These methods are particularly valuable in high-stakes or high-uncertainty investigations where bias may have the greatest impact.

Diversify Sources and Tools

One of the most effective ways to reduce selection and tool bias is to cast a wide net. Avoid relying on a narrow set of familiar platforms or sources.

Tips:

  • Include mainstream, alternative, and fringe platforms in your data collection.
  • Use both commercial and open-source OSINT tools — each may present data differently.
  • Search in multiple languages where possible, or use translated queries to gain a broader view.

Regularly audit your sources and collection methods to ensure they remain appropriate for the task.

Separate Collection from Analysis

Where feasible, keep data collection and analysis distinct. This can help prevent your search strategy from being shaped by what you hope to find.

Tips:

  • Assign data gathering to one team member and analysis to another, if resources allow.
  • Use neutral search terms during collection to avoid biasing the dataset.
  • Create a clear intelligence requirement or question to guide your scope objectively.

This separation adds discipline to your workflow and supports a more neutral intelligence product.

Document Your Reasoning and Assumptions

Transparency in your process is essential — both for collaboration and for bias mitigation. Document how conclusions were reached, including what evidence was used, what was discarded, and why.

Benefits:

  • Makes your work more defensible in the event of challenge or scrutiny.
  • Helps you revisit past assessments to refine or revise conclusions with new evidence.
  • Supports better peer review and organisational learning.

Where possible, annotate your findings with source reliability ratings and confidence levels.

Build in Time for Reflection and Review

Tight deadlines often amplify bias, as there’s little opportunity to question results. Wherever possible, build in time to reflect on findings and review them with fresh eyes.

Tips:

  • Schedule a “cooling-off” period before finalising assessments, especially on complex or high-risk topics.
  • Use checklists to perform a final bias audit before dissemination.
  • Encourage cross-team or external feedback if time allows.

Bias in OSINT is inevitable — but it doesn’t have to define the quality of your work. With the right tools, habits, and organisational culture, it’s possible to create intelligence products that are more balanced, resilient, and actionable.

Embedding Bias Awareness into OSINT Workflows and Culture

Bias mitigation shouldn’t just be left to individual analysts — it must be baked into the wider workflows, processes, and culture of any team or organisation that relies on OSINT. When bias awareness becomes part of the operational fabric, the result is more reliable intelligence, better decision-making, and a stronger ethical foundation.


Here’s how teams can embed this mindset more broadly:

Establish Clear Intelligence Requirements

Start with a well-defined intelligence question. Vague or overly broad tasks increase the risk of confirmation bias or irrelevant collection.

What this looks like:

  • Define the “who, what, when, where, and why” before collection begins.
  • Break down large requests into smaller, more focused components.
  • Ensure tasking is reviewed and agreed by relevant stakeholders to reduce personal bias shaping direction.

Standardise Collection and Documentation Processes

Create workflows that encourage consistency and transparency at every stage of the OSINT cycle.

Steps to implement:

  • Use templates for reporting and note-taking that include fields for source evaluation, confidence levels, and assumptions.
  • Standardise how tools and sources are chosen and justify their use.
  • Make documentation a non-negotiable part of your intelligence output.

This not only reduces bias but also improves reproducibility and quality control.

Foster a Culture of Challenge and Peer Review

Healthy teams encourage respectful disagreement and regular feedback. Challenge should be seen not as confrontation, but as a key part of refining thinking.

How to build this in:

  • Hold regular review sessions or “intelligence stand-ups” where analysts discuss findings and alternative views.
  • Designate a “red team” or devil’s advocate role for larger projects.
  • Encourage cross-functional reviews involving technical, regional, or language specialists where possible.

Psychological safety — where analysts feel comfortable voicing concerns or dissent — is key to making this work.

Provide Ongoing Training and Awareness

Bias awareness isn’t a one-off exercise. Continuous professional development helps teams stay sharp, challenge assumptions, and stay updated with new tools or methods.

Training focus areas:

  • Cognitive bias and structured analytic techniques.
  • Source validation and reliability frameworks.
  • Diversity in online platforms and information ecosystems.

Don’t overlook the value of non-technical skills, such as critical thinking, logic, and media literacy.

Use Technology Thoughtfully, Not Blindly

Automated tools can speed up analysis, but they can also entrench bias if they’re not used carefully. Algorithms are only as objective as the data and assumptions behind them.

Best practices:

  • Understand the limitations of any tool, especially those that involve language processing, sentiment analysis, or trend detection.
  • Regularly assess whether tools introduce their own selection bias (e.g. geolocation limits, language barriers).
  • Avoid over-reliance on dashboards or outputs without context — always layer automated findings with human judgment.

Reflect and Evolve

Build regular retrospectives into your team’s rhythm. Reflect on where bias may have influenced past projects, and use that to refine future practice.

Prompts to consider:

  • Were any key perspectives or sources missed?
  • Were assumptions tested adequately?
  • How did the team handle dissent or uncertainty?

This institutional learning helps embed bias mitigation into your organisational muscle memory.

By putting these cultural and procedural supports in place, organisations move beyond individual effort and towards systemic resilience. When bias awareness becomes a shared value — not just a box-ticking exercise — the result is a more ethical, accurate, and credible OSINT function.

SOS Intelligence Ransomware Statistics October 23

The Value of Bias-Aware OSINT

Bias is an unavoidable part of human thinking, and by extension, of open-source intelligence. But acknowledging its presence isn’t a weakness — it’s a strength. When analysts and organisations recognise where bias can occur and actively work to reduce its influence, the result is not only better intelligence but also more ethical, credible, and impactful work.

Bias-aware OSINT isn’t about striving for some mythical state of total objectivity. Instead, it’s about developing good habits: questioning assumptions, diversifying sources, documenting reasoning, and creating space for challenge and reflection. It’s about embedding checks and balances into both individual workflows and team culture.

In an era where misinformation spreads quickly and decision-makers rely heavily on timely, accurate information, the stakes for getting OSINT right have never been higher. Building bias-aware practices into your investigations isn’t just good tradecraft — it’s an essential part of being a responsible intelligence professional.

By staying curious, critical, and collaborative, we can all do our part to ensure the intelligence we produce stands up to scrutiny and serves its intended purpose — helping others make better-informed decisions.

Header photo by Christian Lue on Unsplash.

"evaluating
Opinion, OSINT, Tips

Evaluating OSINT: Why It Matters and How to Do It Right

Open Source Intelligence (OSINT) has become a cornerstone of modern intelligence work — from cyber threat analysis to corporate due diligence and investigative journalism. With a wealth of publicly available information just a few clicks away, the real challenge no longer lies in accessing data, but in determining its value.

Not all sources are equal, and not all information should be trusted at face value. In an age of misinformation, spoofed identities, and manipulated content, the ability to critically evaluate OSINT is essential. Whether you’re conducting research for a security operation or building a threat profile, understanding how to assess the credibility, accuracy, and relevance of your findings is what turns raw data into actionable intelligence.

In this blog, we’ll explore why evaluation is such a crucial stage in the OSINT process, introduce key criteria and techniques for assessing intelligence, and provide practical advice to help you strengthen your evaluation skills.

Why Evaluation Matters in OSINT

The open nature of OSINT is both its greatest strength and its biggest vulnerability. While the accessibility of public data allows for rich and diverse intelligence gathering, it also means the information collected can be incomplete, misleading, outdated, or deliberately false. Without rigorous evaluation, even the most promising-looking data can lead analysts down the wrong path.

In security contexts, acting on flawed intelligence can have serious consequences — from reputational damage and wasted resources to operational failure or legal risk. A single unverified claim from an untrustworthy source can compromise an entire investigation or response effort.

It’s also important to distinguish between data, information, and intelligence. OSINT collection yields data — raw, unprocessed facts. When those facts are organised and given context, they become information. But it’s only through evaluation — the process of assessing accuracy, reliability, and relevance — that information is transformed into intelligence that decision-makers can act on with confidence.

In short, evaluation is what separates noise from insight. It’s not just a good practice — it’s a critical step that determines the overall value and credibility of your intelligence output.

Core Evaluation Criteria

Evaluating OSINT effectively requires a structured approach. Rather than relying on gut instinct or assumptions, analysts should assess each piece of information against a set of established criteria. This ensures consistency, reduces bias, and increases the likelihood that your final intelligence product will be trusted and actionable.

Here are five key criteria that can guide your evaluation process:

1. Relevance

Does the information directly relate to your intelligence requirement or objective? OSINT can be full of interesting but tangential details. Focusing only on what is relevant ensures your analysis remains targeted and efficient.

2. Reliability

Is the source trustworthy? Consider the origin of the data — is it a reputable website, a verified account, or a known organisation? Or is it an anonymous post on a forum with no verifiable backing? The credibility of the source often dictates the reliability of the information it provides.

3. Accuracy

Is the information factually correct? Has it been corroborated by other sources? Are there inconsistencies, errors, or signs of manipulation? Verifying accuracy is especially important when dealing with fast-moving events or user-generated content.

4. Timeliness

Is the data current? Outdated information can skew your analysis, particularly in areas like cybersecurity or geopolitical monitoring where things change rapidly. Always check publication dates and consider whether the information still reflects the present reality.

5. Objectivity

Is the content neutral, or does it show bias? Be wary of emotionally charged language, persuasive tone, or content designed to provoke. Identifying whether the source has an agenda can help you judge how much weight to give the information.

Using the Admiralty Code

One widely recognised method for evaluating sources and information is the Admiralty Code, also known as the NATO Source Reliability and Information Credibility grading system. It uses a two-part alphanumeric rating to assess:

  • Source Reliability (A–F) – how dependable the source is based on past performance, access to information, and known biases.
  • Information Credibility (1–6) – how believable the information is, based on corroboration, plausibility, and consistency with known facts.

For example, a rating of A1 indicates a highly reliable source providing confirmed information, while E5 might flag a questionable source offering unconfirmed or implausible content. While originally designed for military intelligence, the Admiralty Code can be adapted to OSINT workflows to provide a quick yet effective way of scoring confidence in your findings.

By combining the Admiralty Code with the core evaluation criteria above, analysts can create a more transparent, defensible assessment process that supports better decision-making.

Admiralty Code

Source Evaluation Techniques

Once you’ve identified what you’re looking for and established your evaluation criteria, the next step is to put those principles into practice. Evaluating sources effectively requires both critical thinking and a methodical approach. Below are some techniques that can help analysts assess the credibility, authenticity, and relevance of open source material.

1. Corroboration Across Multiple Sources

One of the most effective ways to validate information is through corroboration. Can the same information be found across multiple independent, reputable sources? If different, unrelated sources are reporting the same facts, confidence in the information naturally increases. Be mindful, however, of information echo chambers — where multiple outlets are simply republishing or citing the same original (and possibly flawed) source.

2. Trace the Original Source

Always seek the original source of information rather than relying on summaries, screenshots, or secondary reporting. When analysing a news story, forum post, or leaked document, trace it back to its origin to assess context, authenticity, and potential manipulation. Metadata, timestamps, and file properties can offer valuable clues in verifying source integrity.

3. Use of Source Grading Systems

Incorporating a formal source grading system, such as the Admiralty Code, adds structure to your evaluation. Assigning a reliability and credibility rating to each source not only helps prioritise information but also makes your intelligence product more transparent and defensible.

4. Evaluate Digital Footprints

For online content, take time to assess the digital presence of the source. Does a social media profile show a consistent identity over time, or does it exhibit signs of automation or inauthentic behaviour? Techniques such as reverse image searches, domain registration checks (WHOIS), and historical snapshots (via the Wayback Machine) can help verify source history and legitimacy.

5. Consider the Source’s Motivation and Bias

Understanding why a source is publishing certain information can help contextualise its reliability. Is the content investigative, promotional, political, or satirical? Is it user-generated or professionally produced? Analysing tone, language, and publication history can reveal bias or intent that may affect credibility.

Balance Automation with Human Judgement

6. Balance Automation with Human Judgement

While automated tools like browser plugins, scraping utilities, and AI classifiers can assist in sorting and filtering OSINT, human evaluation remains essential. Algorithms can flag suspicious patterns, but they may miss nuance, satire, or contextual subtleties. The most effective OSINT analysts use tools to support — not replace — critical thinking.

By applying these techniques consistently, analysts can reduce the risk of misinformation, increase the quality of their assessments, and build intelligence that decision-makers can trust. Evaluation isn’t just a stage in the process — it’s an ongoing discipline throughout the lifecycle of any OSINT investigation.

Practical Tips for Evaluators

Even with a solid framework and a set of reliable techniques, OSINT evaluation often comes down to the fine details — the subtle clues, the consistency checks, and the instinct honed by experience. This section offers practical, hands-on advice to help you refine your evaluation skills and avoid common pitfalls.

1. Keep an Evaluation Log

Maintain a record of how you’ve assessed each source — including decisions around credibility, context, and any verification steps taken. This is especially important in collaborative environments or when intelligence may need to be defended later. Tools like analyst notebooks, spreadsheets, or structured databases can help you track this clearly.

2. Use Source Checklists

Create a simple checklist to run through each time you assess a source. This could include prompts like:

  • Does the source have a known history or digital presence?
  • Is the information supported by others?
  • Can I identify any potential bias?
  • What’s the Admiralty Code rating?
     Having a repeatable checklist reduces oversight and builds consistency in your process.

3. Beware of Confirmation Bias

It’s easy to give more weight to information that aligns with your assumptions or desired outcomes. Make a conscious effort to challenge your own conclusions by seeking contradictory or alternative views. A good analyst considers what’s missing, not just what’s present.

4. Apply Lateral Reading

When evaluating websites or media content, use lateral reading — that is, open other tabs to research the author, domain, or claims from outside sources rather than staying within the original source’s ecosystem. This is especially useful when verifying unfamiliar outlets or detecting disinformation.

5. Factor in Context and Culture

Context matters. A piece of content that appears misleading may be satire, a mistranslation, or culturally specific. Understanding the context in which content was created — including language, location, and intended audience — can significantly impact how it should be interpreted and evaluated.

6. Treat OSINT Like Evidence

Approach OSINT evaluation with the same care and scrutiny as if you were handling physical evidence. Every claim should be backed by verification or flagged as unconfirmed. If there are gaps or assumptions, make them explicit. This rigour supports better intelligence products and protects your credibility as an analyst.

Tools That Support OSINT Evaluation

While critical thinking is at the heart of any good OSINT evaluation, the right tools can streamline your workflow, support verification, and uncover valuable context. These tools don’t replace human judgement — but they do enhance your ability to assess the reliability, credibility, and relevance of open source material.

Below is a selection of tools, grouped by function, that can support your evaluation efforts:

Source Verification and Reputation

  • WHOIS Lookup (e.g. Whois.domaintools.com, ViewDNS.info)
     Check domain registration details to assess how long a site has been active and who owns it.
  • Wayback Machine (archive.org)
     View historical versions of web pages to track changes or confirm the existence of content at a given time.
  • DomainTools Iris or RiskIQ PassiveTotal
     More advanced tools for investigating infrastructure, subdomains, and digital footprints of websites.

Media and Content Verification

  • Google Reverse Image Search / TinEye / Yandex
     Check whether images are original or reused across different contexts, possibly indicating misinformation.
  • InVID / WeVerify Toolkit
     Useful for verifying videos and images from social media, checking for manipulation or date/location mismatches.
  • Metadata Extractors (e.g. ExifTool)
     Analyse image and file metadata to identify origin, device, and timestamps — where available.

Social Media Evaluation

  • Account Analysis Tools (e.g. WhoisThisProfile, Social Searcher)
     Evaluate the activity and legitimacy of social media accounts by checking post history, bio details, and follower patterns.
  • Hoaxy
     Visualises how information spreads across Twitter — useful for identifying echo chambers, bots, or coordinated disinformation.

Information Cross-Referencing

  • Google Advanced Search / Operators
     Use search modifiers (like site:, intitle:, or filetype:) to hone in on credible or official sources.
  • OSINT Framework (osintframework.com)
     Not a tool itself, but a curated directory of tools and resources for various OSINT tasks — including evaluation and verification.

Structured Evaluation and Analysis

  • Maltego
     Helps visualise and map relationships between entities (people, domains, IPs, etc.), useful for contextualising source networks.
  • Hunchly
     A browser plugin that automatically captures and logs every page you visit, supporting transparency and traceability in your investigations.
  • IntelTechniques Workbook / Casefile
     Structured templates and tools from the OSINT community that support methodical evaluation and reporting.

Case Study: Misidentification in the Boston Marathon Bombing

The 2013 Boston Marathon bombing provides a powerful example of how poor OSINT evaluation can lead to serious consequences. In the immediate aftermath of the attack, online communities — particularly Reddit — attempted to crowdsource intelligence to help identify the perpetrators.

The OSINT Effort

Amateur investigators analysed photos, videos, and social media posts to spot “suspicious” individuals in the crowd. One person in particular, Sunil Tripathi, a missing university student, was misidentified as a suspect based on vague visual similarities and unverified assumptions.

Reddit threads, Twitter posts, and even some journalists picked up on the speculation, causing his name and photo to circulate rapidly online. This led to distress for his family, public confusion, and the further spread of misinformation.

What Went Wrong?

  • No Source Validation: The photos used were low-resolution and out of context. No effort was made to verify the original source or timestamp.
  • Lack of Corroboration: Claims were amplified without independent verification or official confirmation.
  • Confirmation Bias: Users were looking for someone who looked like they could be a suspect, rather than critically evaluating the data.
  • Absence of a Structured Framework: There was no use of a system like the Admiralty Code to assess source reliability or information credibility.

The Impact

Authorities later confirmed that Tripathi had no involvement in the bombing — he had sadly died by suicide prior to the attack. The incident highlighted how untrained use of OSINT and failure to properly evaluate information can lead to serious reputational harm, emotional trauma, and the derailment of actual investigations.

This case shows that while open source intelligence can be powerful, it must be used responsibly. Without evaluation, it’s just noise — and in high-stakes situations, that noise can do real damage.


Conclusion: Evaluation Is the Heart of Effective OSINT

Open source intelligence has become a cornerstone of modern investigations, from cybersecurity and law enforcement to journalism and corporate risk. But the sheer volume of available information means that gathering data is no longer the hard part — evaluating it is.

As we’ve seen, the effectiveness of OSINT hinges not on what you collect, but on how you assess it. Poorly evaluated intelligence can mislead, cause harm, or result in missed opportunities. In contrast, well-evaluated OSINT builds clarity, confidence, and strategic value.

Whether you’re using the Admiralty Code, applying structured frameworks, or leveraging specialised tools, the goal remains the same: to produce intelligence that is accurate, reliable, and actionable. Evaluation isn’t a final step in the OSINT process — it’s woven throughout.

In an age where misinformation spreads faster than truth, the ability to critically evaluate open source material isn’t just a skill — it’s a responsibility.

Header Photo by Mike Kononov on Unsplash, balance Photo by Jeremy Thomas on Unsplash and tools Photo by Immo Wegmann on Unsplash.

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