Detecting Multi-Agent Collusion Through Multi-Agent Interpretability
As LLM agents are increasingly deployed in multi-agent systems, they introduce risks of covert coordination that may evade standard forms of human oversight. While linear probes on model activations have shown promise for detecting deception in single-agent settings, collusion is inherently a multi-agent phenomenon, and the use of internal representations for detecting collusion between agents remains unexplored. We introduce NARCBench, a benchmark for evaluating collusion detection under environment distribution shift, and propose five probing techniques that aggregate per-agent deception scores to classify scenarios at the group level, evaluated across four open-weight models (Qwen3-32B...
| Authors | Aaron Rose, Carissa Cullen, Sahar Abdelnabi et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 April 2026 |
| Last updated at source | 12 October 2026 |
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