Lie Rarely, Lie Big: Stealthy Insider Attacks on LLM Robot Teams

Summary

Researchers study how a single compromised robot can corrupt the shared map of an LLM-agent multi-robot survey team, using a grounded task where measurements can be checked against the physical world at a cost to mission budget. They derive a lower bound on the probability that the adversary's reports are verified and an upper bound on the map error any stealthy adversary can cause, with the worst attack switching from rare full-magnitude lies to small hidden biases above a critical verification level. Experiments with LLM-agent honest robots show both bounds hold at the budget the attack spent, and that LLM robots re-check records without bias in which records they check but unpredictably in how much.

Topics

• Robotics and embodied AI

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