When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agents
arXiv:2602.08995v2 Announce Type: replace Abstract: Computer-use agents (CUAs) have made tremendous progress in the past year, yet they still frequently produce misaligned actions that deviate from the user's original intent. Such misaligned actions may arise from external attacks (e.g., indirect prompt injection) or from internal limitations (e.g., erroneous reasoning). They not only expose CUAs to safety risks, but also degrade task efficiency and reliability. This work makes the first effort to define and study misaligned action detection in CUAs, with comprehensive coverage of both externa
The rapid development and deployment of computer-use agents (CUAs) necessitate immediate attention to their reliability and safety, especially as they integrate into critical workflows.
Reliable and safe AI agents are crucial for enterprise adoption and avoiding significant economic and safety risks posed by 'misaligned actions' arising from internal flaws or external attacks like prompt injection.
This work establishes a foundational framework for detecting and correcting misaligned actions in CUAs, shifting focus from mere performance to robust alignment and security at the action level.
- · AI agent developers
- · Cybersecurity firms
- · Enterprises adopting AI agents
- · Users of AI agents
- · Malicious actors
- · Developers of unsecure AI agents
- · Organizations with poor AI governance
Increased trust and adoption of AI agents in sensitive and critical applications.
Development of specialized tools and services for AI agent monitoring and security, fostering a new cybersecurity sub-sector.
Regulatory frameworks emerging to mandate 'misaligned action' detection and correction in commercial AI agent deployments.
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Read at arXiv cs.CL