ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis
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arXiv
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| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866918499309322240 |
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| author | Li, Yu Luo, Haoyu Xie, Yuejin Fu, Yuqian Yang, Zhonghao Shao, Shuai Ren, Qihan Qu, Wanying Fu, Yanwei Yang, Yujiu Shao, Jing Hu, Xia Liu, Dongrui |
| author_facet | Li, Yu Luo, Haoyu Xie, Yuejin Fu, Yuqian Yang, Zhonghao Shao, Shuai Ren, Qihan Qu, Wanying Fu, Yanwei Yang, Yujiu Shao, Jing Hu, Xia Liu, Dongrui |
| contents | Evaluating the safety of LLM-based agents is increasingly important because risks in realistic deployments often emerge over multi-step interactions rather than isolated prompts or final responses. Existing trajectory-level benchmarks remain limited by insufficient interaction diversity, coarse observability of safety failures, and weak long-horizon realism. We introduce ATBench, a trajectory-level benchmark for structured, diverse, and realistic evaluation of agent safety. ATBench organizes agentic risk along three dimensions: risk source, failure mode, and real-world harm. Based on this taxonomy, we construct trajectories with heterogeneous tool pools and a long-context delayed-trigger protocol that captures realistic risk emergence across multiple stages. The benchmark contains 1,000 trajectories (503 safe and 497 unsafe), averaging 9.01 turns and 3.95k tokens, with 1,954 invoked tools drawn from pools spanning 2,084 available tools. Data quality is supported by rule-based and LLM-based filtering plus full human audit. Experiments on frontier LLMs, open-source models, and specialized guard systems show that ATBench is challenging even for strong evaluators, while enabling taxonomy-stratified analysis, cross-benchmark comparison, and diagnosis of long-horizon failure patterns. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02022 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis Li, Yu Luo, Haoyu Xie, Yuejin Fu, Yuqian Yang, Zhonghao Shao, Shuai Ren, Qihan Qu, Wanying Fu, Yanwei Yang, Yujiu Shao, Jing Hu, Xia Liu, Dongrui Artificial Intelligence Evaluating the safety of LLM-based agents is increasingly important because risks in realistic deployments often emerge over multi-step interactions rather than isolated prompts or final responses. Existing trajectory-level benchmarks remain limited by insufficient interaction diversity, coarse observability of safety failures, and weak long-horizon realism. We introduce ATBench, a trajectory-level benchmark for structured, diverse, and realistic evaluation of agent safety. ATBench organizes agentic risk along three dimensions: risk source, failure mode, and real-world harm. Based on this taxonomy, we construct trajectories with heterogeneous tool pools and a long-context delayed-trigger protocol that captures realistic risk emergence across multiple stages. The benchmark contains 1,000 trajectories (503 safe and 497 unsafe), averaging 9.01 turns and 3.95k tokens, with 1,954 invoked tools drawn from pools spanning 2,084 available tools. Data quality is supported by rule-based and LLM-based filtering plus full human audit. Experiments on frontier LLMs, open-source models, and specialized guard systems show that ATBench is challenging even for strong evaluators, while enabling taxonomy-stratified analysis, cross-benchmark comparison, and diagnosis of long-horizon failure patterns. |
| title | ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.02022 |