mlsanityData to 7 Oct 2026

A Systematization of Security Vulnerabilities in Computer Use Agents

arXiv 2507.05445 ↗First posted 7 Jul 2025cs.CRcited by 11

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

Abstract (arXiv)

Computer Use Agents (CUAs), autonomous systems that interact with software interfaces via browsers or virtual machines, are rapidly being deployed in consumer and enterprise environments. These agents introduce novel attack surfaces and trust boundaries that are not captured by traditional threat models. Despite their growing capabilities, the security boundaries of CUAs remain poorly understood. In this paper, we conduct a systematic threat analysis and testing of real-world CUAs under adversarial conditions. We identify seven classes of risks unique to the CUA paradigm, and analyze three concrete exploit scenarios in depth: (1) clickjacking via visual overlays that mislead interface-level reasoning, (2) indirect prompt injection that enables Remote Code Execution (RCE) through chained tool use, and (3) CoT exposure attacks that manipulate implicit interface framing to hijack multi-step reasoning. These case studies reveal deeper architectural flaws across current CUA implementations. Namely, a lack of input provenance tracking, weak interface-action binding, and insufficient control over agent memory and delegation. We conclude by proposing a CUA-specific security evaluation framework and design principles for safe deployment in adversarial and high-stakes settings.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 10 of its 21 references that are in the map. First posted on arXiv 7 Jul 2025.

Builds on

10 of 21 references.

References · 10

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  4. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  5. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
  6. 2022Red Teaming Language Models with Language Modelscited by 983
  7. 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 760
  8. 2023Prompt Injection attack against LLM-integrated Applicationscited by 568
  9. 2024WebVoyager: Building an End-to-End Web Agent with Large Multimodal Modelscited by 424
  10. 2024LLMAuditor: A Framework for Auditing Large Language Models Using Human-in-the-Loopcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 8.

11 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026The Blind Spot of Agent Safety: How Benign User Instructions Expose Critical Vulnerabilities in Computer-Use Agents
  2. 2026SIR: Self-improving Red-teaming for Compute Use Agents
  3. 2026CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents
  4. 2025VPI-Bench: Visual Prompt Injection Attacks for Computer-Use Agents
  5. 2026Invisible Ink Threats: Adversarial Goals Behind Legitimate Tasks in Computer-Use Agents
  6. 2025A Survey on the Safety and Security Threats of Computer-Using Agents: JARVIS or Ultron?
  7. 2026Secure-CUA: Controlling Untrusted Influence in Computer-Use Agents
  8. 2026ADeptS-Bench: Measuring the Trustworthiness of Computer Use Agents Across Devices