arXiv:2606.15034v1 Announce Type: new Abstract: Computer-use agents are increasingly evaluated by whether they complete realistic desktop and web tasks. However, task success alone can miss failures in which an agent reaches the nominal goal through an unsafe shortcut. We introduce OSGuard, a dual-granularity benchmark suite for evaluating safety in computer-use agents under benign, unchanged user instructions. OSGuard contains an action-level benchmark for local guardrail decisions and a risk-augmented execution suite for end-to-end evaluation. The action-level benchmark consists of contextua
The proliferation of AI agents in complex computer environments necessitates robust safety benchmarks to prevent unintended consequences and enhance reliability, aligning with the accelerating deployment of these technologies.
This benchmark is critical for ensuring AI agents are not only effective but also safe, addressing a core concern that could hinder widespread adoption and trust in autonomous systems.
The introduction of OSGuard provides a standardized method for evaluating the safety of computer-use agents, shifting the focus from mere task completion to secure and responsible operation.
- · AI safety researchers
- · Developers of AI agents
- · Enterprises deploying AI agents
- · Cybersecurity sector
- · AI agent developers ignoring safety
- · Systems vulnerable to agent misbehavior
OSGuard will become a standard for developers to test and ensure the safety of their AI agents operating within computer systems.
Increased focus on 'guardrail AI' will lead to more secure and trustworthy agent deployments across various industries.
The benchmark could eventually lead to certification standards or regulatory requirements for AI agent safety in critical applications.
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