Systematic Categorization, Construction and Evaluation of New Attacks against Multi-modal Mobile GUI Agents

The integration of Large Language Models (LLMs) and Multi-modal Large Language Models (MLLMs) into mobile GUI agents has significantly enhanced user efficiency and experience. However, this advancement also introduces potential security vulnerabilities that have yet to be thoroughly explored. In this paper, we present a systematic security investigation of multi-modal mobile GUI agents, addressing this critical gap in the existing literature. Our contributions are twofold: (1) we propose a novel threat modeling methodology, leading to the discovery and feasibility analysis of 34 previously unreported attacks, and (2) we design an attack framework to systematically construct and evaluate these threats. Through a combination of real-world case studies and extensive dataset-driven experiments, we validate the severity and practicality of those attacks, highlighting the pressing need for robust security measures in mobile GUI systems.

@article{yang2025_2407.09295,
  title={ Systematic Categorization, Construction and Evaluation of New Attacks against Multi-modal Mobile GUI Agents },
  author={ Yulong Yang and Xinshan Yang and Shuaidong Li and Chenhao Lin and Zhengyu Zhao and Chao Shen and Tianwei Zhang },
  journal={arXiv preprint arXiv:2407.09295},
  year={ 2025 }
}