LAMEHUG is Python-based information stealer first identified in July 2025 by Ukraine's Computer Emergency Response Team (CERT-UA) in phishing emails targeting Ukrainian government officials. LAMEHUG is the first known malware to integrate artificial intelligence (AI) directly into its attack workflow by querying large language models (LLMs) hosted on Hugging Face to dynamically generate reconnaissance, data theft, and system manipulation commands in real time. LAMEHUG has been attributed to APT28. [1][2][3]
25
TechniquesTop 12%
7
Tactics68th pct
1
Threat groupunranked
0
CampaignsNone recorded
Enterprise ATT&CK only. Any Mobile or ICS rows on the same ATT&CK page are not carried.
LAMEHUG payloads have been disguised with legitimate looking filenames including AI_generator_uncensored_Canvas_PRO_v0.9.exe and AI_image_generator_v0.95.exe.1011
LAMEHUG can exfiltrate collected system information and documents to C2.1213
Exfiltration16 detection rules
Standing S9035
Reach is how much of ATT&CK this malware touches. Coverage is how well defended each thing it does is, as a median per technique rather than a total — a total would just restate the reach. Each figure is ranked against all 825 ATT&CK software entries only where that population actually spreads. Where most of the population shares one value, a percentile would rank the tie instead of the entity, so the raw value is shown and no rank is claimed.
Reach
Techniques25Top 12%
88th percentile · 88% of 825 ATT&CK software entries have this many Enterprise techniques or fewer.
Tactics768th pct
68th percentile · 68% of 825 ATT&CK software entries have this many tactics spanned or fewer.
Threat group161% tie · 22 values
61% of the population shares a single value across only 22 distinct values, so a percentile here would rank the tie, not the entity.
Campaigns085% tie · 8 values
85% of the population shares a single value across only 8 distinct values, so a percentile here would rank the tie, not the entity.
Coverage
Rules / technique25med61st pct
61st percentile · 61% of 825 ATT&CK software entries have this many detection rules per technique or fewer.
Tests / technique8med72nd pct
72nd percentile · 72% of 825 ATT&CK software entries have this many atomic tests per technique or fewer.
Data comps / technique4med54% tie · 11 values
54% of the population shares a single value across only 11 distinct values, so a percentile here would rank the tie, not the entity.
Mitigations / technique1med57th pct
57th percentile · 57% of 825 ATT&CK software entries have this many mitigations per technique or fewer.
5 of 8 axes carry an honest rank on this page. Coverage medians are taken across this malware’s 25 Enterprise techniques.
Range
Technique Campaign use over its cumulative rule count.
Detects when AppLocker "Audit only" enforcement mode reports that an Application, DLL, Script, MSI, or Packaged-App would have been blocked if AppLocker "Enforce rules" enforcement mode was enabled.
The following analytic detects the execution of tools commonly exploited by cybercriminals, such as those used for unauthorized access, network scanning, privilege escalation, password dumping or data exfiltration. (...)
Detects the Linux-specific execution chain of the plain-crypto-js malicious npm dependency by Axios NPM package, including payload download via curl and detached execution using nohup and python3. (...)
The following analytic detects the creation of a batch file (.bat) within the Windows system directory tree, specifically in the System32 or SysWOW64 folders. (...)
The following analytic detects the use of CertUtil.exe with the 'decode' argument, which may indicate an attempt to decode a previously encoded file, potentially containing malicious payloads. (...)
Adversaries may communicate using application layer protocols associated with web traffic to avoid detection/network filtering by blending in with existing traffic. (...)
The following analytic identifies the execution of the "whoami" command with the "/group" flag, where the results are passed to the "find" command in order to look for a the string "12288". (...)
This analytic detects potential reconnaissance activities on Cisco ASA devices by identifying execution of multiple information-gathering "show" commands within a short timeframe. (...)