MIP against Agent: Malicious Image Patches Hijacking Multimodal OS Agents
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
Recent advances in operating system (OS) agents have enabled vision-language models (VLMs) to directly control a user's computer. Unlike conventional VLMs that passively output text, OS agents autonomously perform computer-based tasks in response to a single user prompt. OS agents do so by capturing, parsing, and analysing screenshots and executing low-level actions via application programming interfaces (APIs), such as mouse clicks and keyboard inputs. This direct interaction with the OS significantly raises the stakes, as failures or manipulations can have immediate and tangible consequences. In this work, we uncover a novel attack vector against these OS agents: Malicious Image Patches (MIPs), adversarially perturbed screen regions that, when captured by an OS agent, induce it to perform harmful actions by exploiting specific APIs. For instance, a MIP can be embedded in a desktop wallpaper or shared on social media to cause an OS agent to exfiltrate sensitive user data. We show that MIPs generalise across user prompts and screen configurations, and that they can hijack multiple OS agents even during the execution of benign instructions. These findings expose critical security vulnerabilities in OS agents that have to be carefully addressed before their widespread deployment.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 55 of its 101 references that are in the map. First posted on arXiv 13 Mar 2025.
Builds on
55 of 101 references.
Closest ancestors · 7
Same direction, newest first
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Other references · 48
- 2017Attention Is All You Needcited by 60,361
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- 2013Intriguing properties of neural networkscited by 5,155
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- 2023On Evaluating Adversarial Robustness of Large Vision-Language Modelscited by 284
- 2018Adversarial Attacks and Defences: A Surveycited by 259
- 2024Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scalecited by 223
- 2024APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasetscited by 198
- 2024OmniParser for Pure Vision Based GUI Agentcited by 198
- 2023Image Hijacks: Adversarial Images can Control Generative Models at Runtimecited by 172
- 2024UFO: A UI-Focused Agent for Windows OS Interactioncited by 148
- 2022Towards Adversarial Attack on Vision-Language Pre-training Modelscited by 144
- 2024Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Modelscited by 120
- 2024xLAM: A Family of Large Action Models to Empower AI Agent Systemscited by 111
- 2023Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Modelscited by 108
- 2021Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attackscited by 101
- 2023On the Robustness of Large Multimodal Models Against Image Adversarial Attackscited by 92
- 2020Improving Image Captioning with Better Use of Captionscited by 84
- 2024Best-of-N Jailbreakingcited by 76
- 2024ToolGen: Unified Tool Retrieval and Calling via Generationcited by 74
- 2023How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defensescited by 66
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Built on by
Citing papers per year: 2025: 3; 2026: 9.
12 papers.
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- 2026Directional Embedding Smoothing for Robust Vision Language Modelscited by 0
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- 2026MobileWorldSafety: Benchmarking GUI Agent Safety Against Environmental Injection Attacks in Android Appscited by 0
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- 2026Securing Computer-Use Agents Against Branch Steering Attackscited by 0
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- 2026MobileWorldSafety: Benchmarking GUI Agent Safety Against Environmental Injection Attacks in Android Apps18 Aug 2026
- 2026Untrusted Content Masking for Web Agents with Security Guarantees6 Jul 2026
- 2026Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability8 May 2026
- 2026Preference Redirection via Attention Concentration: An Attack on Computer Use Agents9 Apr 2026
- 2026Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey30 Mar 2026
- 2026Directional Embedding Smoothing for Robust Vision Language Models16 Mar 2026
- 2026CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents14 Jan 2026
- 2025Cross-Modal Content Optimization for Steering Web Agent Preferences4 Oct 2025
- 2025Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents4 May 2025
- 2025Manipulating Multimodal Agents via Cross-Modal Prompt Injection19 Apr 2025
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