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    <abstract>Existing agent-safety evaluation has focused mainly on externally induced risks. Yet agents may still enter unsafe trajectories under benign conditions. We study this complementary but underexplored setting through the lens of intrinsic risk, where intrinsic failures remain latent, propagate across long-horizon execution, and eventually lead to high-consequence outcomes. To evaluate this setting, we introduce non-attack intrinsic risk auditing, a guard-oriented safety evaluation task, and present HINTBench, a benchmark of 596 agent trajectories, comprising 400 synthetic risky trajectories, 136 synthetic safe trajectories, 30 reconstructed real-world risky trajectories, and 30 reconstructed real-world safe trajectories, with an average length of 24.0 steps. HINTBench supports three tasks: risk detection, risk-step localization, and intrinsic failure-type identification, with annotations organized under a unified five-constraint taxonomy. Experiments reveal a substantial capability gap: strong LLMs perform well on trajectory-level risk detection, but the best model remains below 37 on fine-grained Strict-F1 for risk-step localization. Existing off-the-shelf guard models evaluated under their native prompts transfer poorly to this setting. These findings establish intrinsic risk auditing as an open challenge for agent safety.</abstract>
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%0 Conference Proceedings
%T HINTBench: Horizon-agent Intrinsic Non-attack Trajectory Benchmark
%A Wang, Jiacheng
%A Hou, Jinchang
%A Wang, Fabian
%A Jian, Ping
%A Lv, Zhonghou
%A Bao, Chenfu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-hintbench
%X Existing agent-safety evaluation has focused mainly on externally induced risks. Yet agents may still enter unsafe trajectories under benign conditions. We study this complementary but underexplored setting through the lens of intrinsic risk, where intrinsic failures remain latent, propagate across long-horizon execution, and eventually lead to high-consequence outcomes. To evaluate this setting, we introduce non-attack intrinsic risk auditing, a guard-oriented safety evaluation task, and present HINTBench, a benchmark of 596 agent trajectories, comprising 400 synthetic risky trajectories, 136 synthetic safe trajectories, 30 reconstructed real-world risky trajectories, and 30 reconstructed real-world safe trajectories, with an average length of 24.0 steps. HINTBench supports three tasks: risk detection, risk-step localization, and intrinsic failure-type identification, with annotations organized under a unified five-constraint taxonomy. Experiments reveal a substantial capability gap: strong LLMs perform well on trajectory-level risk detection, but the best model remains below 37 on fine-grained Strict-F1 for risk-step localization. Existing off-the-shelf guard models evaluated under their native prompts transfer poorly to this setting. These findings establish intrinsic risk auditing as an open challenge for agent safety.
%R 10.18653/2026.findings-emnlp.1639
%U https://aclanthology.org/2026.findings-emnlp.1639/
%U https://doi.org/10.18653/2026.findings-emnlp.1639
%P 34862-34888

Markdown (Informal)

[HINTBench: Horizon-agent Intrinsic Non-attack Trajectory Benchmark](https://aclanthology.org/2026.findings-emnlp.1639/) (Wang et al., Findings 2026)

• HINTBench: Horizon-agent Intrinsic Non-attack Trajectory Benchmark (Wang et al., Findings 2026)

ACL

• Jiacheng Wang, Jinchang Hou, Fabian Wang, Ping Jian, Zhonghou Lv, and Chenfu Bao. 2026. HINTBench: Horizon-agent Intrinsic Non-attack Trajectory Benchmark. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 34862–34888, Budapest, Hungary. Association for Computational Linguistics.