arXiv:2505.14418v3 Announce Type: replace Abstract: Graphical user interface (GUI) agents powered by multimodal large language models (MLLMs) have shown greater promise for human-interaction. However, due to the high fine-tuning cost, users often rely on open-source GUI agents or APIs offered by AI providers, which introduces a critical but underexplored supply chain threat: backdoor attacks. In this work, we first unveil that MLLM-powered GUI agents naturally expose multiple interaction-level triggers, such as historical steps, environment states, and task progress. Based on this observation,
The proliferation of MLLM-powered GUI agents, often built on open-source components or third-party APIs, creates new attack surfaces that are now being actively explored as these systems become more integrated into daily operations.
This highlights a critical and under-addressed supply chain vulnerability in AI, impacting the trustworthiness and security of autonomous agents that interact directly with users and systems.
The focus shifts beyond traditional model security to the entire operational lifecycle of AI agents, including their interaction-level triggers and dependencies on external components, demanding new security paradigms.
- · AI security firms
- · Developers of robust MLLM-powered GUI agents
- · Cybersecurity researchers
- · Users relying on unverified open-source GUI agents
- · AI providers with lax security practices
- · Companies with high-value digital assets managed by vulnerable AI agents
Increased scrutiny and demand for robust security audits of MLLM-powered GUI agents becomes immediate.
New security standards and best practices for AI agent development, deployment, and supply chain management will emerge.
Certification and 'trust score' systems for AI agents may become commonplace, influencing adoption and market fragmentation based on security posture.
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Read at arXiv cs.CL