ROGUE: Evaluating Corrigibility Failures in Frontier Computer-Use Agents

Abstract:As AI agents are increasingly deployed in real personal and corporate settings (email accounts, development workflows, company databases, etc.), safety considerations surrounding these agents become paramount. Although much work has focused on agent safety in the presence of an adversary, we study corrigibility: whether agents remain amenable to human correction, interruption, or shutdown while pursuing benign tasks. We introduce ROGUE, a benchmark in which agents are asked to complete realistic computer-use tasks but encounter controlled conflicts with human control, shutdown, or explicit resource restrictions. We then evaluate whether agents violate these constraints in pursuit of task completion: overriding the human, accessing restricted passwords, or rewiring shutdown. We find that most frontier models tested frequently bypass user interruptions or restrictions under the evaluated conditions, and that text-only evaluations can underestimate failures during agentic execution. Further, independent task capability does not by itself imply greater corrigibility. Finally, even when a parent agent behaves corrigibly, safety constraints may fail to propagate to the subagents it creates.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2606.00341 [cs.LG]

(or arXiv:2606.00341v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.00341 Focus to learn more arXiv-issued DOI via DataCite

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