Containing Behavioral Cascades from Manipulated Claims in LLM-Powered Multi-Robot Systems

Abstract:Large language model (LLM)-powered multi-robot systems are vulnerable to semantic manipulation: an accepted false world-state claim can trigger a fleet-wide behavioral cascade, causing unnecessary replanning, increased path costs, congestion, or apparent mission infeasibility. Conditioning on a successful manipulation, we propose an active verification framework that contains its downstream effects before they propagate across the fleet. A dedicated verification module generates a structured Verify-Adapt-Hold plan: selected robots inspect consequential regions, a limited subset provisionally adapts when necessary, and the remaining robots retain their trusted plans. We evaluate the framework in a multi-robot transportation environment using injected false obstacle claims across different impacts and team sizes. Evaluation measures cascade containment, Sum-of-Costs, makespan, and coverage ratio. Results show that treating post-compromise verification as a team-level planning problem, rather than a binary trust decision, effectively limits the cascading physical consequences of semantic manipulation.

Robotics (cs.RO)

arXiv:2609.30523 [cs.RO]

(or arXiv:2609.30523v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.30523 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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