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Detecting collusion through multi-agent interpretability
TL;DR Prior work has shown that linear probes are effective at detecting deception in singular LLM agents. Our work extends this use to multi-agent settings, where we aggregate the activations of groups of interacting agents in order to detect collusion. We propose five probing techniques, underpinned by the distributed anomaly detection taxonomy, and train and evaluate them on NARCBench - a novel open-source three tier collusion benchmark Paper | Code Introducing the problem LLM agents are being increasingly deployed in multi-agent settings (e.g., software engineering through agentic coding or financial analysis of a stock) and with this poses a significant safety risk through potential covert coordination. Agents has been shown to try to steer outcomes/suppress information for their own
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