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Building an Adversarial Consensus Engine | Multi-Agent LLMs for Automated Malware Analysis

0.586
Chimera Difficulty Score
a synthesis of Flesch-Kincaid, Coleman-Liau, SMOG, and Dale-Chall readability metrics
Executive Summary • Large Language Models can perform static malware analysis, but individual tool runs produce unreliable results contaminated by decompiler artifacts, dead code, and hallucinated capabilities. • We built a multi-agent architecture for reversing macOS malware that treats each reverse engineering tool (radare2, Ghidra, Binary Ninja, IDA Pro) as an independent, skeptical analyst in ...
This multi-agent consensus pipeline represents an innovative approach to malware analysis, leveraging the power of AI ensembles and educational content analysis. By treating each reverse engineering tool as an independent analyst with a mandate to challenge other tools' claims, this pipeline aims to produce more reliable and accurate reports on malware threats. The structure of the pipeline—which includes bridge scripts, subagents, and an Orchestrator—emphasizes process over individual models' r...