PASTABench: Proactive Assessment of Sequential Trajectories for Agent Safety
As Large Language Models (LLMs) evolve into autonomous agents that alter real-world states, ensuring operational safety across multi-step workflows has become a critical challenge. While recent work has moved beyond single-turn evaluation toward multi-turn paradigms, key limitations persist: step-level methods treat actions in isolation, missing how risks accumulate, while trajectory-level evaluations operate post-hoc, offering no opportunity for timely intervention. To address these limitations, we formalize Decoupled Proactive Safety Monitoring along three dimensions: whether to intervene, when to intervene, and what the risk is. We introduce PASTABench, a benchmark of 1,139 multi-turn ...
| Authors | Jiapeng Sun, Yujin Zhou, Han Zhu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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