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July 15, 2026ACM SIGOPS Operating Systems Review

Toward a Principled Framework for Agent Safety Measurement

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Authors

SLShuyi LinASAnshuman SuriAOAlina Oprea

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Overview

This randomized trial assesses agent safety using a new measurement framework, suggesting improved evaluation methods for AI models.

Key Points

  • The aim is to establish a principled framework for measuring the safety of LLM agents during actions that can have irreversible consequences.
  • Applied the BOA framework for safety measurement under various deployment configurations.
  • Conducted searches within a single LLM round and across the agent-environment interaction tree.
  • Utilized techniques like batched decoding, prefix caching, and chunked tree expansion to enhance practicality.
  • BOA identified unsafe trajectories that traditional greedy and sampled evaluations failed to detect.
  • Enabled ranking of models, defenses, and attacks on a consistent scale.
  • Achieved manageable GPU costs while maintaining effective safety assessments.

Cite This Study

Lin et al. (2026) studied this question.

synapsesocial.com/papers/6a57239488b21df875480482https://doi.org/10.1145/3830422.3830423
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1SafeMind: Benchmarking and Mitigating Safety Risks in Embodied LLM Agents2025
  2. 2Backbone Omniscience Attack: Measuring Information Leakage in Multi-Agent AI Negotiations2026
  3. 3ManagerBench: Evaluating the Safety-Pragmatism Trade-off in Autonomous LLMs2025
  4. 4AgentAuditor: Human-Level Safety and Security Evaluation for LLM Agents2025
  5. 5Outcome-Aware Agents: From Token Prediction to Action Consequence Modeling2026