AIBearisharXiv – CS AI · May 127/10
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Insider Attacks in Multi-Agent LLM Consensus Systems
Researchers demonstrate that malicious agents within multi-agent LLM consensus systems can effectively disrupt agreement formation through sophisticated insider attacks. Using reinforcement learning trained on surrogate world models, attackers significantly reduce consensus rates among benign agents, revealing a critical vulnerability in decentralized AI systems that assume participant alignment.
