dorsal/arxiv
View SchemaCaMeLs Can Use Computers Too: System-level Security for Computer Use Agents
| Authors | Hanna Foerster, Robert Mullins, Tom Blanchard, Nicolas Papernot, Kristina Nikolić, Florian Tramèr, Ilia Shumailov, Cheng Zhang, Yiren Zhao |
|---|---|
| Categories | |
| ArXiv ID | 2601.09923vv1 |
| URL | https://arxiv.org/abs/2601.09923 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss. The only known robust defense is architectural isolation that strictly separates trusted task planning from untrusted environment observations. However, applying this design to Computer Use Agents (CUAs) -- systems that automate tasks by viewing screens and executing actions -- presents a fundamental challenge: current agents require continuous observation of UI state to determine each action, conflicting with the isolation required for security. We resolve this tension by demonstrating that UI workflows, while dynamic, are structurally predictable. We introduce Single-Shot Planning for CUAs, where a trusted planner generates a complete execution graph with conditional branches before any observation of potentially malicious content, providing provable control flow integrity guarantees against arbitrary instruction injections. Although this architectural isolation successfully prevents instruction injections, we show that additional measures are needed to prevent Branch Steering attacks, which manipulate UI elements to trigger unintended valid paths within the plan. We evaluate our design on OSWorld, and retain up to 57% of the performance of frontier models while improving performance for smaller open-source models by up to 19%, demonstrating that rigorous security and utility can coexist in CUAs.
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"date_created": "2026-02-17T05:53:24.268000Z",
"date_modified": "2026-02-17T05:53:24.268000Z",
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"abstract": "AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss. The only known robust defense is architectural isolation that strictly separates trusted task planning from untrusted environment observations. However, applying this design to Computer Use Agents (CUAs) -- systems that automate tasks by viewing screens and executing actions -- presents a fundamental challenge: current agents require continuous observation of UI state to determine each action, conflicting with the isolation required for security. We resolve this tension by demonstrating that UI workflows, while dynamic, are structurally predictable. We introduce Single-Shot Planning for CUAs, where a trusted planner generates a complete execution graph with conditional branches before any observation of potentially malicious content, providing provable control flow integrity guarantees against arbitrary instruction injections. Although this architectural isolation successfully prevents instruction injections, we show that additional measures are needed to prevent Branch Steering attacks, which manipulate UI elements to trigger unintended valid paths within the plan. We evaluate our design on OSWorld, and retain up to 57% of the performance of frontier models while improving performance for smaller open-source models by up to 19%, demonstrating that rigorous security and utility can coexist in CUAs.",
"arxiv_id": "2601.09923",
"authors": [
"Hanna Foerster",
"Robert Mullins",
"Tom Blanchard",
"Nicolas Papernot",
"Kristina Nikoli\u0107",
"Florian Tram\u00e8r",
"Ilia Shumailov",
"Cheng Zhang",
"Yiren Zhao"
],
"categories": [
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents",
"url": "https://arxiv.org/abs/2601.09923",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "70675e01-18de-47fc-8272-a7616a84a9d0",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
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"user_id": 1000002
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