AI-Generated Junk Code Used to Obfuscate Malware Logic, Evade Static Analysis
Researchers identified a malware campaign using large volumes of AI-generated junk code to inflate binary size and evade static analysis, obscuring credential-harvesting and C2 functionality targeting Windows endpoints. The technique leverages LLM output to produce syntactically valid but functionally inert code at scale, degrading signature-based detection without requiring manual obfuscation expertise. SOC teams should prioritize behavioral detection, ASR rule enforcement, and full credential rotation on affected systems.