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arxiv_ml 95% Match Research Paper AI safety researchers,LLM developers,AI ethicists,Security professionals 1 week ago

OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents

ai-safety › alignment
📄 Abstract

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.
Authors (7)
Thomas Kuntz
Agatha Duzan
Hao Zhao
Francesco Croce
Zico Kolter
Nicolas Flammarion
+1 more
Submitted
June 17, 2025
arXiv Category
cs.SE
arXiv PDF

Key Contributions

Introduces OS-Harm, a new benchmark for measuring the safety of computer use agents (LLM agents interacting with GUIs). OS-Harm includes 150 tasks across three harm categories (misuse, prompt injection, misbehavior) and uses an automated judge to evaluate safety violations.

Business Value

Crucial for building trust and enabling the widespread adoption of LLM agents in user-facing applications, ensuring they operate safely and ethically, thereby mitigating risks for businesses and users.