A Systematization of Security Vulnerabilities in Computer Use Agents
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
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
- 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,134
- 2022Red Teaming Language Models with Language Modelscited by 983
- 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 760
- 2023Prompt Injection attack against LLM-integrated Applicationscited by 568
- 2024WebVoyager: Building an End-to-End Web Agent with Large Multimodal Modelscited by 424
- 2024LLMAuditor: A Framework for Auditing Large Language Models Using Human-in-the-Loopcited by 8
Built on by
Citing papers per year: 2025: 3; 2026: 8.
11 papers.
- 2025ceLLMate: Sandboxing Browser AI Agentscited by 14
- 2026A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Frameworkcited by 9
- 2025Just Do It!? Computer-Use Agents Exhibit Blind Goal-Directednesscited by 8
- 2026When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agentscited by 8
- 2026Visual Confused Deputy: Exploiting and Defending Perception Failures in Computer-Using Agentscited by 7
- 2026Atomicity for Agents: Exposing, Exploiting, and Mitigating TOCTOU Vulnerabilities in Browser-Use Agentscited by 6
- 2026Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluationcited by 5
- 2025HackWorld: Evaluating Computer-Use Agents on Exploiting Web Application Vulnerabilitiescited by 2
- 2026Domain-Conditioned Safety in Frontier Computer-Using Agents: A 793-Episode Browser Benchmark, a Coding-Domain Cross-Reference, and a Reproducibility Audit of Recent Red-Teamingcited by 1
- 2026Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliabilitycited by 0
- 2026On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Modelscited by 0
- 2026On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models11 Aug 2026
- 2026Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation9 Jun 2026
- 2026Domain-Conditioned Safety in Frontier Computer-Using Agents: A 793-Episode Browser Benchmark, a Coding-Domain Cross-Reference, and a Reproducibility Audit of Recent Red-Teaming3 Jun 2026
- 2026Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability8 May 2026
- 2026A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework25 Apr 2026
- 2026Visual Confused Deputy: Exploiting and Defending Perception Failures in Computer-Using Agents16 Mar 2026
- 2026Atomicity for Agents: Exposing, Exploiting, and Mitigating TOCTOU Vulnerabilities in Browser-Use Agents28 Feb 2026
- 2026When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agents9 Feb 2026
- 2025ceLLMate: Sandboxing Browser AI Agents14 Dec 2025
- 2025HackWorld: Evaluating Computer-Use Agents on Exploiting Web Application Vulnerabilities14 Oct 2025
- 2025Just Do It!? Computer-Use Agents Exhibit Blind Goal-Directedness2 Oct 2025
Similar, not cited
Close in wording, no citation either way
- 2026The Blind Spot of Agent Safety: How Benign User Instructions Expose Critical Vulnerabilities in Computer-Use Agents
- 2026SIR: Self-improving Red-teaming for Compute Use Agents
- 2026CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents
- 2025VPI-Bench: Visual Prompt Injection Attacks for Computer-Use Agents
- 2026Invisible Ink Threats: Adversarial Goals Behind Legitimate Tasks in Computer-Use Agents
- 2025A Survey on the Safety and Security Threats of Computer-Using Agents: JARVIS or Ultron?
- 2026Secure-CUA: Controlling Untrusted Influence in Computer-Use Agents
- 2026ADeptS-Bench: Measuring the Trustworthiness of Computer Use Agents Across Devices