mlsanityData to 14 Oct 2026

MIP against Agent: Malicious Image Patches Hijacking Multimodal OS Agents

arXiv 2503.10809 ↗First posted 13 Mar 2025cs.CR cs.LGcited by 12

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

  1. 2024Agent-SafetyBench: Evaluating the Safety of LLM Agents19 Dec
  2. 2024Attacking Vision-Language Computer Agents via Pop-ups4 Nov
  3. 2024Imprompter: Tricking LLM Agents into Improper Tool Use19 Oct
  4. 2024AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents11 Oct
  5. 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases17 Jul
  6. 2024Dissecting Adversarial Robustness of Multimodal LM Agents18 Jun
  7. 2024Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast13 Feb

Other references · 48

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  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2024The Llama 3 Herd of Modelscited by 14,715
  5. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  6. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  7. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  8. 2013Intriguing properties of neural networkscited by 5,155
  9. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,332
  10. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
  11. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,947
  12. 2016Adversarial examples in the physical worldcited by 1,769
36 more
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  2. 2024HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusalcited by 1,429
  3. 2023Jailbreaking Black Box Large Language Models in Twenty Queriescited by 1,420
  4. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
  5. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,268
  6. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,195
  7. 2019Adversarial Examples Are Not Bugs, They Are Featurescited by 953
  8. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 546
  9. 2023Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4Vcited by 299
  10. 2023On Evaluating Adversarial Robustness of Large Vision-Language Modelscited by 284
  11. 2018Adversarial Attacks and Defences: A Surveycited by 259
  12. 2024Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scalecited by 223
  13. 2024APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasetscited by 198
  14. 2024OmniParser for Pure Vision Based GUI Agentcited by 198
  15. 2023Image Hijacks: Adversarial Images can Control Generative Models at Runtimecited by 172
  16. 2024UFO: A UI-Focused Agent for Windows OS Interactioncited by 148
  17. 2022Towards Adversarial Attack on Vision-Language Pre-training Modelscited by 144
  18. 2024Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Modelscited by 120
  19. 2024xLAM: A Family of Large Action Models to Empower AI Agent Systemscited by 111
  20. 2023Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Modelscited by 108
  21. 2021Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attackscited by 101
  22. 2023On the Robustness of Large Multimodal Models Against Image Adversarial Attackscited by 92
  23. 2020Improving Image Captioning with Better Use of Captionscited by 84
  24. 2024Best-of-N Jailbreakingcited by 76
  25. 2024ToolGen: Unified Tool Retrieval and Calling via Generationcited by 74
  26. 2023How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defensescited by 66
  27. 2023SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentationcited by 39
  28. 2024CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agentscited by 39
  29. 2024Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attackscited by 39
  30. 2022Calibrated Interpretation: Confidence Estimation in Semantic Parsingcited by 33
  31. 2024Failures to Find Transferable Image Jailbreaks Between Vision-Language Modelscited by 30
  32. 2024Gradient-based Jailbreak Images for Multimodal Fusion Modelscited by 14
  33. 2024Adversarial Attacks to Multi-Modal Modelscited by 12
  34. 2025Shh, don't say that! Domain Certification in LLMscited by 7
  35. 2024Jailbreak Defense in a Narrow Domain: Limitations of Existing Methods and a New Transcript-Classifier Approachcited by 6
  36. 2023A reading survey on adversarial machine learning: Adversarial attacks and their understandingcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 9.

12 papers.

Similar, not cited

Close in wording, no citation either way

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  2. 2026SnapGuard: Lightweight Prompt Injection Detection for Screenshot-Based Web Agents
  3. 2025VisualTrap: A Stealthy Backdoor Attack on GUI Agents via Visual Grounding Manipulation
  4. 2026MMSkillRisk: Can Agents Stay Safe When Multimodal Skills Become Traps?
  5. 2025Invisible to Humans, Triggered by Agents: Stealthy Jailbreak Attacks on Mobile Vision-Language Agents
  6. 2026MIRAGE: Context-Aware Prompt Injection against Mobile GUI Agents via User-Generated Content
  7. 2026Are GUI Agents Focused Enough? Automated Distraction via Semantic-level UI Element Injection
  8. 2026AgentRAE: Remote Action Execution through Notification-based Visual Backdoors against Screenshots-based Mobile GUI Agents