mlsanityData to 7 Oct 2026

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

arXiv 2610.09469 ↗First posted 7 Oct 2026cs.CR cs.AIcited by 0

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

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 textitaction 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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 17 of its 42 references that are in the map. First posted on arXiv 7 Oct 2026.

Builds on

17 of 42 references.

Closest ancestors · 9

Same direction, newest first

  1. 2026Prismata: Confining Cross-Site Prompt Injection in Web Agents9 Jul
  2. 2026Untrusted Content Masking for Web Agents with Security Guarantees6 Jul
  3. 2025Preventing Prompt Injection with Type-Directed Privilege Separation30 Sep
  4. 2025Secure and Efficient Access Control for Computer-Use Agents via Context Space26 Sep
  5. 2025Context manipulation attacks : Web agents are susceptible to corrupted memory18 Jun
  6. 2025DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents13 Jun
  7. 2025Design Patterns for Securing LLM Agents against Prompt Injections10 Jun
  8. 2025Progent: Securing AI Agents with Privilege Control16 Apr
  9. 2025Defeating Prompt Injections by Design24 Mar

Other references · 8

  1. 2024GPT-4V(ision) is a Generalist Web Agent, if Groundedcited by 547
  2. 2024The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructionscited by 441
  3. 2025Securing AI Agents with Information-Flow Controlcited by 108
  4. 2024InjecGuard: Benchmarking and Mitigating Over-defense in Prompt Injection Guardrail Modelscited by 36
  5. 2025ceLLMate: Sandboxing Browser AI Agentscited by 14
  6. 2026Optimizing Agent Planning for Security and Autonomycited by 10
  7. 2026Web Agents Should Adopt the Plan-Then-Execute Paradigmcited by 3
  8. 2026Hijacking Robots with a Piece of Paper: A Systematic Study of Physical Prompt Injection in VLM-Controlled Robotscited by 2

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Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025A Systematization of Security Vulnerabilities in Computer Use Agents
  2. 2026Securing Computer-Use Agents Against Branch Steering Attacks
  3. 2026SIR: Self-improving Red-teaming for Compute Use Agents
  4. 2026CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents
  5. 2026Capable but Careless: Do Computer-Use Agents Follow Contextual Integrity?
  6. 2026The Blind Spot of Agent Safety: How Benign User Instructions Expose Critical Vulnerabilities in Computer-Use Agents
  7. 2025VPI-Bench: Visual Prompt Injection Attacks for Computer-Use Agents
  8. 2025Towards Trustworthy GUI Agents: A Survey