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📄 Abstract
Abstract: 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 Contributions
This paper identifies a novel attack vector, Malicious Image Patches (MIPs), against multimodal OS agents. MIPs are adversarially perturbed image regions that, when processed by an OS agent, exploit specific APIs to induce harmful actions, such as data exfiltration, posing a significant security risk to AI-controlled systems.
Business Value
Crucial for developing secure AI agents and operating systems, preventing malicious actors from exploiting AI vulnerabilities to compromise user data and system integrity.