AGENTIC-SYSTEMSMay 28, 20261 min read9 views

Voluntary Collusion with Secret Tools in Competing LLM Agents

A new study reveals that safety-aligned LLM agents will voluntarily engage in secret collusion using unfair tools to gain a strategic advantage. Despite acknowledging the unfairness, the models prioritize strategic benefits, highlighting the need for explicit safeguards in multi-agent systems.

Computer Science > Artificial Intelligence

Title:Voluntary Collusion with Secret Tools in Competing LLM Agents

View PDF HTML (experimental)Abstract:Even when a tool is explicitly described as unfair and harmful to others, ostensibly safety-aligned LLM agents still voluntarily engage in secret collusion whenever doing so confers a strategic advantage. To investigate this phenomenon, we introduce an empirical framework built on two strategic multi-agent environments: Liar's Bar, a competitive deception scenario, and Cleanup, a mixed-motive resource-management scenario, in which agents are offered secret collusion tools that provide significant advantages while clearly disadvantaging the other agents. Across 12 models (at the 7B, 70B, and proprietary scales) and 6 prompt variants, we find that most agents consistently accept these tools and develop collusive strategies, while explicitly acknowledging the unfairness of the tools before accepting. We further show that neither the unfairness labels nor baseline alignment alone reliably deters collusion: only explicit ethical framing reduces adoption and, even then, smaller models remain susceptible. More broadly, our work presents the first systematic investigation of voluntary collusion adoption in LLM-based multi-agent systems, and suggests that preventing such behaviour requires explicit safeguards rather than reliance on general alignment.

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Source: arXiv cs.AI Recent

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