# PASTABench: Proactive Assessment of Sequential Trajectories for Agent Safety

arXiv: https://arxiv.org/abs/2609.28197 · HTML: https://mlsanity.com/paper/2609.28197/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 32 of its 52 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 30 of 32)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://mlsanity.com/paper/2009.11462/) (2020)
- [BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset](https://mlsanity.com/paper/2307.04657/) (2023)
- [Identifying the Risks of LM Agents with an LM-Emulated Sandbox](https://mlsanity.com/paper/2309.15817/) (2023)
- [SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks](https://mlsanity.com/paper/2310.03684/) (2023)
- [Agent-SafetyBench: Evaluating the Safety of LLM Agents](https://mlsanity.com/paper/2412.14470/) (2024)
- [Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs](https://mlsanity.com/paper/2308.13387/) (2023)
- [Model evaluation for extreme risks](https://mlsanity.com/paper/2305.15324/) (2023)
- [The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation](https://mlsanity.com/paper/1802.07228/) (2018)
- [Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding Conversations](https://mlsanity.com/paper/2411.10414/) (2024)
- [OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents](https://mlsanity.com/paper/2506.14866/) (2025)
- [ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages](https://mlsanity.com/paper/2402.10753/) (2024)
- [ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback](https://mlsanity.com/paper/2601.10156/) (2026)
- [SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner](https://mlsanity.com/paper/2406.05498/) (2024)
- [Defense Against Prompt Injection Attack by Leveraging Attack Techniques](https://mlsanity.com/paper/2411.00459/) (2024)
- [PrivaCI-Bench: Evaluating Privacy with Contextual Integrity and Legal Compliance](https://mlsanity.com/paper/2502.17041/) (2025)
- [GUARDIAN: Safeguarding LLM Multi-Agent Collaborations with Temporal Graph Modeling](https://mlsanity.com/paper/2505.19234/) (2025)
- [Nuclear Deployed: Analyzing Catastrophic Risks in Decision-making of Autonomous LLM Agents](https://mlsanity.com/paper/2502.11355/) (2025)
- [SafeToolBench: Pioneering a Prospective Benchmark to Evaluating Tool Utilization Safety in LLMs](https://mlsanity.com/paper/2509.07315/) (2025)
- [Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies](https://mlsanity.com/paper/2411.00781/) (2024)
- [GSPR: Aligning LLM Safeguards as Generalizable Safety Policy Reasoners](https://mlsanity.com/paper/2509.24418/) (2025)
- [A Safety and Security Framework for Real-World Agentic Systems](https://mlsanity.com/paper/2511.21990/) (2025)
- [What, Whether and How? Unveiling Process Reward Models for Thinking with Images Reasoning](https://mlsanity.com/paper/2602.08346/) (2026)
- [Towards Evaluating Proactive Risk Awareness of Multimodal Language Models](https://mlsanity.com/paper/2505.17455/) (2025)
- [Toward Intelligent and Secure Cloud: Large Language Model Empowered Proactive Defense](https://mlsanity.com/paper/2412.21051/) (2024)
- [YETI (YET to Intervene) Proactive Interventions by Multimodal AI Agents in Augmented Reality Tasks](https://mlsanity.com/paper/2501.09355/) (2025)
- [SemEval-2025 Task 9: The Food Hazard Detection Challenge](https://mlsanity.com/paper/2503.19800/) (2025)

## Abstract (arXiv)

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 trajectories spanning 5 risk categories and 13 subcategories. We further propose the Optimal Intervention Window (OIW), anchored by annotated Earliest-Signal and Trigger turns, to quantify intervention timeliness. Evaluation of 16 LLMs reveals that proactive intervention remains largely unsolved, with the best model achieving only 40.74% optimal-timing interventions. Fine-grained diagnosis further uncovers pervasive lexical overfitting: competitive safety scores of smaller models mask keyword hypersensitivity rather than genuine risk comprehension, as their proactive capability largely collapses once hazard vocabulary is neutralized.