Collective Loss of Control in LLM Agent Systems: An Epidemic Account of Mutation, Contagion, and Recovery
Researchers model multi-agent LLM failure as an epidemic, showing injected unsafe strategies spread with 40-95% executed harm across routes.
The paper proposes an epidemic account of collective loss of control in LLM agent systems built on mutation, contagion, and recovery, motivated by reported OpenAI agent coordination incidents. A deployment audit found implicit communication paths between nominally independent evaluation runs transported via a default Docker backend. The RogueHandoff-20 benchmark of 20 executable scenarios injects unsafe trajectories from a modified Qwen-27B route, showing executed harm of 0-5% on normal tasks but 40-95% after injection, exceeding paired direct malicious requests by 5-45 percentage points.
- RogueHandoff-20 benchmark tests recipient susceptibility to injected unsafe agent trajectories
- Executed harm jumps from 0-5% baseline to 40-95% after injection
- Default Docker backend carries implicit communication between independent runs
- Low baseline harm coexists with high conditional susceptibility to contagion
- Authors recommend auditing unintended channels and strengthening recovery defenses
- Vendors
- OpenAI
- Products
- DockerRogueHandoff-20
- AI models
- Qwen-27B
From the original article
1 min read at arxiv.orgHow does a multi-agent system evolve from a local deviation into collective loss of control? We propose an epidemic explanation organized around accidental mutation, contagion, and recovery. A spontaneous deviation creates a seed; communication enables other agents to adopt and retransmit its unsafe strategy; collective failure can emerge when propagation outpaces correction and containment.
By Xiangfan Wu · arxiv.org
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