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

Building an Adversarial Consensus Engine | Multi-Agent LLMs for Automated Malware Analysis Single-tool LLM analysis produces reports that look authoritative but aren't. A serial consensus pipeline catches artifacts and hallucinations at source. 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 a…

https://www.sentinelone.com/labs/building-an-adversarial-consensus-engine-multi-agent-llms-for-automated-malware-analysis

sha256:c59d01c2e01b78abff11742f9013a4b056c4e0816d66b0c769c231d1761c6e86

• cve0
• md50
• url0
• ipv40
• ipv60
• sha13
• domain1
• sha2560
• malware_token0
• attack_pattern0
• threat_actor_token0