LLM-generated malware evades endpoint detection — AI-assisted polymorphic code
Date: 2024-04
Affected: Endpoint detection platforms — traditional signature and behavioural detection methods
Attack vector: malware
Category: research-demonstrated
Severity: Critical

Security researchers demonstrated that LLMs could generate polymorphic malware variants that evade traditional signature-based and behavioural endpoint detection. By iteratively prompting the model to rewrite malware code with different obfuscation techniques, API call patterns, and control flow while maintaining functionality, the generated variants bypassed detection by major EDR platforms including CrowdStrike, SentinelOne, and Microsoft Defender.

Impact: Fundamental challenge to signature-based detection; arms race between AI-generated attacks and AI-assisted defence

Mitigations:
AI-assisted malware detection that analyses semantic intent, not just signatures
Behavioural analysis at runtime rather than static code analysis
Rate limiting and abuse monitoring for code generation API endpoints
Refusal training for explicit malware generation requests

Architectural layers (MAESTRO):
label: Foundation Models
notes: Model generates functional malware code with obfuscation on request
role: origin
label: Agent Frameworks
notes: Iterative prompting creates evolving variants that evade detection
role: propagation
label: Security & Compliance
notes: Traditional endpoint detection rendered ineffective against AI-generated variants
role: impact