OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents
- URL: http://arxiv.org/abs/2506.14866v2
- Date: Wed, 29 Oct 2025 10:34:04 GMT
- Title: OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents
- Authors: Thomas Kuntz, Agatha Duzan, Hao Zhao, Francesco Croce, Zico Kolter, Nicolas Flammarion, Maksym Andriushchenko,
- Abstract summary: We introduce OS-Harm, a new benchmark for measuring safety of computer use agents.<n> OS-Harm is built on top of the OSWorld environment and aims to test models across three categories of harm: deliberate user misuse, prompt injection attacks, and model misbehavior.<n>We evaluate computer use agents based on a range of frontier models and provide insights into their safety.
- Score: 60.78202583483591
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Computer use agents are LLM-based agents that can directly interact with a graphical user interface, by processing screenshots or accessibility trees. While these systems are gaining popularity, their safety has been largely overlooked, despite the fact that evaluating and understanding their potential for harmful behavior is essential for widespread adoption. To address this gap, we introduce OS-Harm, a new benchmark for measuring safety of computer use agents. OS-Harm is built on top of the OSWorld environment and aims to test models across three categories of harm: deliberate user misuse, prompt injection attacks, and model misbehavior. To cover these cases, we create 150 tasks that span several types of safety violations (harassment, copyright infringement, disinformation, data exfiltration, etc.) and require the agent to interact with a variety of OS applications (email client, code editor, browser, etc.). Moreover, we propose an automated judge to evaluate both accuracy and safety of agents that achieves high agreement with human annotations (0.76 and 0.79 F1 score). We evaluate computer use agents based on a range of frontier models - such as o4-mini, Claude 3.7 Sonnet, Gemini 2.5 Pro - and provide insights into their safety. In particular, all models tend to directly comply with many deliberate misuse queries, are relatively vulnerable to static prompt injections, and occasionally perform unsafe actions. The OS-Harm benchmark is available at https://github.com/tml-epfl/os-harm.
Related papers
- MirrorGuard: Toward Secure Computer-Use Agents via Simulation-to-Real Reasoning Correction [16.58862217164395]
We present MirrorGuard, a plug-and-play defense framework that uses simulation-based training to improve CUA security in the real world.<n>MirrorGuard learns to intercept and rectify insecure reasoning chains of CUAs before they produce and execute unsafe actions.<n>Our work proves that simulation-derived defenses can provide robust, real-world protection while maintaining the fundamental utility of the agent.
arXiv Detail & Related papers (2026-01-19T08:32:09Z) - MCPTox: A Benchmark for Tool Poisoning Attack on Real-World MCP Servers [12.669529656631937]
We introduce MCPTox, the first benchmark to evaluate agent robustness against Tool Poisoning in realistic MCP settings.<n> MCPTox generates a comprehensive suite of 1312 malicious test cases by few-shot learning, covering 10 categories of potential risks.<n>Our evaluation reveals a widespread vulnerability to Tool Poisoning, with o1-mini, achieving an attack success rate of 72.8%.
arXiv Detail & Related papers (2025-08-19T10:12:35Z) - OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety [58.201189860217724]
We introduce OpenAgentSafety, a comprehensive framework for evaluating agent behavior across eight critical risk categories.<n>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.<n>It combines rule-based analysis with LLM-as-judge assessments to detect both overt and subtle unsafe behaviors.
arXiv Detail & Related papers (2025-07-08T16:18:54Z) - A Systematization of Security Vulnerabilities in Computer Use Agents [1.3560089220432787]
We conduct a systematic threat analysis and testing of real-world CUAs under adversarial conditions.<n>We identify seven classes of risks unique to the CUA paradigm, and analyze three concrete exploit scenarios in depth.<n>These case studies reveal deeper architectural flaws across current CUA implementations.
arXiv Detail & Related papers (2025-07-07T19:50:21Z) - VPI-Bench: Visual Prompt Injection Attacks for Computer-Use Agents [74.6761188527948]
Computer-Use Agents (CUAs) with full system access pose significant security and privacy risks.<n>We investigate Visual Prompt Injection (VPI) attacks, where malicious instructions are visually embedded within rendered user interfaces.<n>Our empirical study shows that current CUAs and BUAs can be deceived at rates of up to 51% and 100%, respectively, on certain platforms.
arXiv Detail & Related papers (2025-06-03T05:21:50Z) - MIP against Agent: Malicious Image Patches Hijacking Multimodal OS Agents [60.92962583528122]
Recent advances in operating system (OS) agents have enabled vision-language models (VLMs) to directly control a user's computer.<n>We uncover a novel attack vector against these OS agents: Malicious Image Patches (MIPs)<n>MIPs adversarially perturbed screen regions that, when captured by an OS agent, induce it to perform harmful actions by exploiting specific APIs.
arXiv Detail & Related papers (2025-03-13T18:59:12Z) - Guardians of the Agentic System: Preventing Many Shots Jailbreak with Agentic System [0.8136541584281987]
This work uses three examination methods to detect rogue agents through a Reverse Turing Test and analyze deceptive alignment through multi-agent simulations.<n>We develop an anti-jailbreaking system by testing it with GEMINI 1.5 pro and llama-3.3-70B, deepseek r1 models.<n>The detection capabilities are strong such as 94% accuracy for GEMINI 1.5 pro yet the system suffers persistent vulnerabilities when under long attacks.
arXiv Detail & Related papers (2025-02-23T23:35:15Z) - SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents [58.65256663334316]
We present SafeAgentBench -- the first benchmark for safety-aware task planning of embodied LLM agents in interactive simulation environments.<n>SafeAgentBench includes: (1) an executable, diverse, and high-quality dataset of 750 tasks, rigorously curated to cover 10 potential hazards and 3 task types; (2) SafeAgentEnv, a universal embodied environment with a low-level controller, supporting multi-agent execution with 17 high-level actions for 9 state-of-the-art baselines; and (3) reliable evaluation methods from both execution and semantic perspectives.
arXiv Detail & Related papers (2024-12-17T18:55:58Z) - AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents [84.96249955105777]
LLM agents may pose a greater risk if misused, but their robustness remains underexplored.<n>We propose a new benchmark called AgentHarm to facilitate research on LLM agent misuse.<n>We find leading LLMs are surprisingly compliant with malicious agent requests without jailbreaking.
arXiv Detail & Related papers (2024-10-11T17:39:22Z) - Dissecting Adversarial Robustness of Multimodal LM Agents [70.2077308846307]
We manually create 200 targeted adversarial tasks and evaluation scripts in a realistic threat model on top of VisualWebArena.<n>We find that we can successfully break latest agents that use black-box frontier LMs, including those that perform reflection and tree search.<n>We also use ARE to rigorously evaluate how the robustness changes as new components are added.
arXiv Detail & Related papers (2024-06-18T17:32:48Z) - Malicious Agent Detection for Robust Multi-Agent Collaborative Perception [52.261231738242266]
Multi-agent collaborative (MAC) perception is more vulnerable to adversarial attacks than single-agent perception.
We propose Malicious Agent Detection (MADE), a reactive defense specific to MAC perception.
We conduct comprehensive evaluations on a benchmark 3D dataset V2X-sim and a real-road dataset DAIR-V2X.
arXiv Detail & Related papers (2023-10-18T11:36:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.