TRACES: Proactive Safety Auditing for Multi-Turn LLM Agents via Trajectory-State Modeling

Abstract:LLM agents increasingly operate through multi-turn tool use and environment interaction, where safety risks often emerge from intermediate steps long before they surface in the final outcome. Reactive auditing is therefore insufficient: post-hoc diagnosis frequently misses the chance to flag risks while they are unfolding. We propose TRACES, a representation-based proactive auditor that learns prefix-level trajectory risk states from the hidden representations of an observer LLM. TRACES induces latent mechanism features from step representations and models their temporal evolution to estimate whether a partial trajectory is drifting toward unsafe behavior. To sidestep the cost and ambiguity of step-level risk annotation, TRACES is trained with weak trajectory-level supervision while still producing dense prefix-level risk estimates. Across multiple agent safety benchmarks, TRACES improves both full-trajectory safety prediction and proactive risk discrimination. Our analyses further suggest that these risk states can help train a safer agent, highlighting the broader potential of proactive auditing for long-horizon agent safety.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2605.27690 [cs.CL]

(or arXiv:2605.27690v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.27690 Focus to learn more arXiv-issued DOI via DataCite

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