Secure-CUA: Controlling Untrusted Influence in Computer-Use Agents

Sarthak Choudhary and Somesh Jha (UW-Madison) with Mihai Christodorescu, Ashish Hooda, Tongxin Li and Damien Octeau (Google and Google DeepMind) present Secure-CUA, a design for computer-use agents that limits what untrusted interface content can influence.
Ask this paper
Threat. GUIs mix trusted controls with untrusted content. An adversary controlling that content can inject instructions or misleading visual cues that change the agent's action or redirect a click to the wrong target.
Requirements. The paper formalizes security requirements for both the agent's decision and its GUI execution, and shows that enforcing both at every step protects the execution trace in an ideal model.
Action transactions. Before seeing untrusted content, the agent commits to a per-action program that fixes which queries it will ask about that content and how answers may be used. An isolated query model answers the queries, untrusted regions are masked, and the target is located on the masked interface.
Utility. On 400 WebArena tasks with three frontier models and five seeds (6,000 traces), Secure-CUA reaches 53.55% task success against 55.12% for an unprotected agent and 13.17% for a CaMeL-style agent.
Query use. 19.6% of transactions issue queries, averaging 11.0 queries per task; 52.4% ask for structured values.
Abstract
Computer-use agents (CUAs) perform tasks across applications (such as desktops, mobile apps, and web browsers) by observing graphical interfaces and issuing commands such as clicks and keystrokes. These interfaces combine trusted controls and content with untrusted content needed for legitimate tasks. An adversary controlling this untrusted content can embed instructions or misleading visual cues to change the agent's intended action or redirect its commands to the wrong interface target. We formalize security requirements for both the agent's decisions and their execution through GUI commands. In an ideal execution model, we show that enforcing both requirements at each step protects execution traces. We instantiate this model in Secure-CUA, our system for secure CUA execution. Its key idea is to commit to an explicit per-action program, called an $\textit{action transaction}$, before accessing untrusted content. Each transaction fixes its queries to untrusted content and the permitted uses of their responses. The system masks untrusted regions and evaluates each transaction to produce the next action, using an isolated query model to answer its queries. It then locates the intended interface target using the masked interface. Under the model's assumptions, Secure-CUA is secure by design, while generating a new transaction at each step helps maintain high task utility by adapting to changing interfaces. We evaluate Secure-CUA under benign conditions on 400 WebArena tasks using three frontier models across $5$ seeds, yielding $6,000$ execution traces. Secure-CUA achieves an average task success rate of $53.55\%$, compared with $55.12\%$ for Vanilla-CUA and $13.17\%$ for CaMeL-CUA.