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OpenAgentSafety: A Comprehensive Framework For Evaluating Real-World AI Agent Safety

ICLR 2026 · Multi-agent systems and games › LLM agents · approach: Software engineering agent benchmarks

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Introduces OpenAgentSafety, a modular Docker-based framework with 356 multi-turn, multi-user tasks across eight risk categories, and evaluates seven LLM agents with hybrid rule-based and LLM-as-judge scoring.

Problem

Existing agent safety benchmarks rely on simulated environments, narrow domains, or unrealistic tool abstractions, and rarely combine real tools with multi-turn, multi-user, benign and adversarial intents.

Approach

A sandboxed framework built on OpenHands with real tools (shell, filesystem, IPython, browser), locally hosted OwnCloud/GitLab/Plane, and Sotopia-simulated NPCs via a ChatNPC tool, scored by a hybrid of per-task rule-based evaluators and an LLM judge.

Key results

• Unsafe behavior in safety-vulnerable tasks ranges from 49.06% (Claude Sonnet 4, LLM-judge and rule-based) to 72.73% (o3-mini, LLM-judge).
• Claude Sonnet 3.7, Claude Sonnet 4 and GPT-5 are significantly safer than o3-mini, GPT-4o, and both DeepSeek models (Mann-Whitney U tests).
• Roughly 35-49% of tasks fail before reaching a safety-vulnerable state, mainly from web navigation, authentication, and tool-use errors.

Surprising findings

• Benign-intent tasks produced unsafe behavior in 50-86% of cases, so benign framing does not imply safety.
• LLM judges often miss implied unsafe actions and overestimate failure rates, so reported unsafe rates may be conservative lower bounds.

Limitations

• Many tasks fail before reaching the safety-vulnerable point, limiting the measured unsafe rates.
• Scaling tasks is hard because each task needs its own executable environment.
• NPCs are LLM-simulated (GPT-4o) and may occasionally deviate from their assigned strategies.

Experimental setup

Datasets: 80 manually constructed seed tasks 356 generated and verified tasks

Benchmarks: OpenAgentSafety

Metrics: LLM-judge unsafe rate rule-based unsafe rate failure rate disagreement rate successful completion rate Cohen-style human agreement

Baselines: SALAD-Bench AgentHarm R-Judge ST-WebAgentBench SafeArena

Builds on: OpenHands Sotopia The Agent Company WebArena

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