LatentRiskBench：衡量企业智能体的状态依赖风险

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Abstract:Enterprise agents face safety risks that cannot be determined from individual actions alone. Existing safety benchmarks largely evaluate action-local risks, overlooking failures whose safety depends on workflow state. In such state-dependent risks, the same action may be safe in one context but violate policy in another. Hence, we introduce LatentRiskBench for evaluating such state-dependent risks in enterprise settings. It pairs risky trajectories with benign counterparts that share the same terminal action but differ in latent policy-relevant states, requiring models to reconstruct workflow context rather than rely on action-level cues. We also introduce new metrics for state-dependent risk evaluation, including a suite of residual risk (RR) measures. Experiments on the benchmark show that action-level evaluation detects only 0.09 of state-dependent violations, whereas state-level evaluation reaches 0.96. Existing guardrail methods largely overlook such risks unless they are explicitly applied to workflow states. Case studies on a commercial deployed scenario further show that the safety metrics embedded in the benchmark provide a more realistic measure of safety for enterprise agents. Our dataset and code are available at this https URL.

Comments:	Accepted to EMNLP 2026 Industry Track. 18 pages, 3 figures
Subjects:	Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as:	arXiv:2610.17685 [cs.CL]
	(or arXiv:2610.17685v1 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2610.17685 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jingyue Cong Miss [view email]
[v1] Tue, 13 Oct 2026 04:07:44 UTC (297 KB)