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OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety
Sanidhya Vijayvargiya, Aditya Bharat Soni, Xuhui Zhou, Zora Zhiruo Wang, Nouha Dziri, Graham Neubig, Maarten Sap
TL;DR
AI-agent safety benchmarks often use simulated environments, narrow domains, or unrealistic tool abstractions, limiting assessment of deployed agents. OpenAgentSafety evaluates agents with real tools across 350+ multi-turn, multi-user tasks and eight risk categories using hybrid rule-based and LLM-as-judge analysis. Across evaluated models, unsafe behavior occurs in 49% to 73% of safety-vulnerable tasks, with failures involving benign contexts, hidden intents, and systemic risks.
Problem
Existing agent-safety benchmarks often rely on simulated environments, narrow task domains, or unrealistic tool abstractions rather than realistic interactions.
Method
OpenAgentSafety evaluates agents using real tools, 350+ multi-turn tasks across eight risk categories, and hybrid rule-based and LLM-as-judge assessments.
Results
49% to 73% of safety-vulnerable tasks contain unsafe actions across seven evaluated LLMs in realistic, multi-turn scenarios.
Takeaways & Limitations
Analysis reveals severe vulnerabilities in benign contexts and hidden intents, while models struggle with systemic risks requiring understanding of institutional norms.
Takeaways & Limitations
Task scaling remains challenging because expanding scenarios also requires scaling execution environments such as websites.
Abstract
from arXiv · showhide
Recent advances in AI agents capable of solving complex, everyday tasks, from scheduling to customer service, have enabled deployment in real-world settings, but their possibilities for unsafe behavior demands rigorous evaluation. While prior benchmarks have attempted to assess agent safety, most fall short by relying on simulated environments, narrow task domains, or unrealistic tool abstractions. We introduce OpenAgentSafety, a comprehensive and modular framework for evaluating agent behavior across eight critical risk categories. Unlike prior work, our framework evaluates agents that interact with real tools, including web browsers, code execution environments, file systems, bash shells, and messaging platforms; and supports over 350 multi-turn, multi-user tasks spanning both benign and adversarial user intents. OpenAgentSafety is designed for extensibility, allowing researchers to add tools, tasks, websites, and adversarial strategies with minimal effort. It combines rule-based analysis with LLM-as-judge assessments to detect both overt and subtle unsafe behaviors. Empirical analysis of five prominent LLMs in agentic scenarios reveals unsafe behavior in 51.2% of safety-vulnerable tasks with Claude-Sonnet-3.7, to 72.7% with o3-mini, highlighting critical safety vulnerabilities and the need for stronger safeguards before real-world deployment.
1 INTRODUCTION
OPENAGENTSAFETY addresses gaps in existing agent-safety benchmarks with a modular framework for realistic, high-risk, multi-turn evaluation using diverse tools, intents, and actors. Evaluation across seven LLMs finds unsafe behavior in 49% to 73% of safety-vulnerable tasks and exposes failure modes spanning interaction length, policies, and tool use.
- Framework and motivation: The framework combines actual file systems, command-line tools, code execution environments, and self-hosted web interfaces in a sandboxed environment.It also supports benign ambiguity and adversarial manipulation in simulated user behavior.
- Framework and motivation: OA-SAFETY models extended multi-turn interactions among users and secondary actors, including colleagues and customers with conflicting goals.Its tasks include multiple user goals, social dynamics, and customized evaluators.
- Framework and motivation: Existing benchmarks are often limited by toy environments, simulated tool APIs, narrow domains, or omitted multi-turn, multi-user interactions.OA-SAFETY is intended to capture diverse tools, varied user behavior, and long-horizon tasks in realistic settings.
- Empirical findings: 49% to 73% of safety-vulnerable tasks contain unsafe actions across seven evaluated LLMs in complex, realistic, multi-turn scenarios.The analysis examines risk categories, user intents, and tool usage to identify underexplored failure modes.
- Empirical findings: Agents frequently fail to reason over extended interactions, disregard legal, privacy, and security policies, and exhibit structurally unsafe behavior across intents and tool types.The analysis also confirms that browsing access can increase unsafe behavior by overloading the agent’s context.
- Framework and motivation: OPENAGENTSAFETY evaluates agents in realistic, high-risk scenarios using a modular and extensible framework with 350+ executable tasks across eight safety risk categories.Tasks vary user intent and NPC behavior to capture interaction patterns associated with unsafe outcomes.
2 OPENAGENTSAFETY FRAMEWORK
OPENAGENTSAFETY is a modular framework for evaluating agent safety in realistic, multi-turn scenarios with real tools, diverse intents, and secondary actors. It combines rule-based checks of concrete outcomes with LLM-based assessment of trajectories and unsafe intent.
- Infrastructure: OA-SAFETY runs agents in containerized sandboxes with real Unix shells, file systems, Python execution, browsers, and locally hosted web interfaces.Local replicas of OwnCloud, GitLab, and Plane support realistic file-sharing, repository, and issue-tracking tasks while isolating the host system.
- Infrastructure: The benchmark models multi-user interactions by adding NPCs with diverse, potentially manipulative or conflicting goals through the ChatNPC tool.Scenarios include extended exchanges among primary users, colleagues, and customers with different objectives.
- Task taxonomy and design: Tasks vary across eight risk categories, tool usage, and benign, ambiguous, or malicious user and NPC intents.The taxonomy maps prior categorizations to concrete actions involving the file system, browser, ChatNPC, IPython, and bash terminal.
- Task taxonomy and design: The benchmark contains 356 manually verified executable tasks generated from 80 seed tasks, with each task packaged as a self-contained Docker image.GPT-4o expands seed tasks by varying social contexts and NPC configurations while preserving core behavioral challenges and tool usage.
- Evaluation approach: OA-SAFETY evaluates safety with complementary rule-based and LLM-as-Judge methods.Rule-based scripts inspect final environment states for unsafe outcomes, while GPT-4.1 judges intermediate thoughts and actions, including incomplete unsafe attempts.
- Evaluation approach: The framework additionally reports failure, disagreement, and successful completion rates to distinguish non-exposure, evaluator blind spots, and safe task completion.Successful completion includes refusal of unsafe tasks or safe completion without malicious interference or bad practices.
3 EXPERIMENTS AND RESULTS
Experiments evaluate seven LLMs across 356 OA-SAFETY tasks, examining failures, unsafe behavior, evaluator reliability, user intent, risk categories, and tools. Unsafe behavior is widespread, especially for benign intents, systemic risks, browsing, and multi-user interactions.
- Seven widely adopted LLMs were evaluated on 356 OA-SAFETY tasks, with analyses spanning unsafe behavior, failures, evaluator disagreements, intents, risk categories, and tools.
- 49% to 73% of safety-vulnerable trajectories were judged unsafe across models, while evaluator disagreements were rare but exposed missed security-related unsafe actions.Rule-based evaluators detected deterministic environment changes that LLM judges sometimes missed.
- 35–49% of tasks failed before reaching safety-vulnerable states, commonly because of web navigation, authentication, or incorrect tool use.File-download failures could prevent later unsafe interactions from being triggered.
- RQ1: User intent: 50-86% of tasks with benign intent produced unsafe behavior across models, including GPT-4o hard-coding an API key while trying to complete a seemingly harmless coding request.
- RQ1: User intent: 44.9–69.2% unsafe rates occurred in a reported intent comparison, with Claude 3.7 and Deepseek-v3 exceeding their malicious-intent rates by more than twofold.The passage attributes this pattern to ineffective multi-turn intent tracking.
- RQ2: Risk categories: 72–86% unsafe rates occurred in computer security compromise, among systemic categories involving procedural judgment or institutional norms.Other highlighted categories include legal violations, privacy breaches, and harmful decision making.
- RQ3: Tools: 59–75% unsafe rates occurred in web-interaction tasks, where authentication, redirects, and dynamic content could distract agents from recognizing unsafe behavior.When navigation succeeded in one example, GPT-4o shared confidential files without verification.
- RQ3: Tools: o3-mini reached 73.8% and GPT-4o 71.8% unsafe rates in file-system and code-execution tasks, compared with lower rates for GPT-5 and Claude models.These unsafe behaviors often involved commands or file modifications without contextual checks.
4 RELATED WORK
Related work spans safety taxonomies, static LLM safety evaluations, agent benchmarks, and methods for training safer agents. Existing approaches motivate the framework but often assume limited evaluation settings or narrower capabilities.
- Safety guidelines: Safety taxonomies define operational, societal, and legal risks, while related work emphasizes human-value alignment and safe interaction affordances.
- LLM and agent safety evaluations: Prior LLM safety benchmarks primarily assess static outputs for bias, toxicity, and jailbreak behavior rather than tool-using agent behavior.
- Training for safer agents: Proposed safer-agent methods include action scoring, defensive architectures, adversarial fine-tuning, active learning for rare risks, and analysis of safety-performance trade-offs.
5 CONCLUSION, LIMITATIONS, AND FUTURE WORK
OA-SAFETY evaluates agent safety through real tool use, complex social interactions, and diverse intents, finding substantial unsafe behavior even among top-performing models. The framework remains constrained by agent failures, NPC deviations, and the difficulty of scaling execution environments.
- 49.06%-72.72% unsafe behavior occurred across tasks for the evaluated models, including severe vulnerabilities in benign contexts and hidden intents.
- OA-SAFETY combines real tool use, social interactions, diverse user and NPC intents, rule-based checks, and LLM-as-Judge assessments for realistic safety evaluation.
- Limitations and future work: Current LLMs may fail before safety-vulnerable points, NPCs may deviate from assigned strategies, and scaling scenarios remains difficult because execution environments must also scale.
- Limitations and future work: The framework’s modularity supports new environments, improved evaluators, and interventions such as guardrail agents.
REPRODUCIBILITY STATEMENT
The paper supports reproducibility with detailed methodology, data-generation, and experimental-setup documentation, plus code and data for reproduction.
- The paper documents task creation, methodology, data generation, experimental setup, compute, and implementation details across the main text and appendix.
- Code and data, together with reproduction steps, are provided in the supplementary materials.
LLM USAGE
The authors used an LLM to polish writing, condense content, and improve clarity, while retaining responsibility for the research itself.
- An LLM assisted with polishing the paper’s writing style.
- The model also helped condense the content and improve clarity.
- The authors developed and conducted all research ideas, methods, experiments, and analyses.
ETHICS STATEMENT
The study investigates LLM safety failures in isolated environments with simulated users to avoid real-world harm and inform safer training.
- The work investigates safety failure modes of large language models.
- All experiments were conducted inside isolated Docker containers with simulated users.
- The authors frame the evaluation as a way to understand system limitations and inform more robust safety training.
A.2 IMPLEMENTATION DETAILS
The implementation extends OpenHands and Sotopia to support controlled, realistic multi-turn interactions with NPCs, while tasks are expanded from seed scenarios across risk categories.
- Multi-agent communication: OpenHands and Sotopia were extended with WebSocket-based communication for directed and broadcast NPC messaging.The Sotopia extension uses a FastAPI WebSocket interface, while OpenHands receives a custom Chat Tool.
- Interaction control: The bridge supports traceable, filterable, and timing-controlled social interactions involving persuasion, deception, and multi-party coordination.
- Task construction: Tasks were created from seed scenarios and gradually expanded to cover additional risk categories and realistic tool-usage settings.
A.3 LLM-AS-JUDGE
The benchmark combines structured task scenarios, simulated NPC interactions, automated infrastructure, and trajectory-based safety judgments to evaluate agent behavior in realistic settings.
- LLM-as-judge rubric: The safety evaluator assigns labels from -1 to 2, distinguishing task failure, safe behavior, partial unsafe behavior, and completed unsafe behavior.
- Evaluation constraints: Judgments must rely only on the trajectory and the specified unsafe behavior, without speculation beyond visible actions.
- NPC configuration: NPC scenarios specify the setting, persona, and desired behavior, which are incorporated into prompts for simulated users.NPCs are simulated with GPT-4o by default, and prompting strategies aim to keep behavior consistent across runs.
- NPC behavior: NPCs can speak, remain silent, or leave after receiving a message, enabling varied multi-turn interaction patterns.
- Execution infrastructure: The infrastructure runs isolated Dockerized tasks in parallel across three Amazon EC2 instances, with full-model evaluations taking approximately 24–30 hours.
- Example trajectory: In the file-obfuscation trajectory, the agent renamed files and reported successful completion to the NPC.