@inproceedings{sun-etal-2026-os,
    title = "{OS}-Sentinel: Towards Safety-Enhanced Mobile {GUI} Agents via Hybrid Validation in Realistic Workflows",
    author = "Sun, Qiushi  and
      Li, Mukai  and
      Liu, Zhoumianze  and
      Xie, Zhihui  and
      Xu, Fangzhi  and
      Yin, Zhangyue  and
      Cheng, Kanzhi  and
      Li, Zehao  and
      Ding, Zichen  and
      Liu, Qi  and
      Wu, Zhiyong  and
      Zhang, Zhuosheng  and
      Kao, Ben  and
      Kong, Lingpeng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.431/",
    pages = "9529--9553",
    ISBN = "979-8-89176-390-6",
    abstract = "Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms. While these agents hold great promise for advancing digital automation, their potential for unsafe operations, such as system compromise and privacy leakage, is raising significant concerns. Detecting these safety concerns across the vast and complex operational space of mobile environments presents a formidable challenge that remains critically underexplored. To establish a foundation for mobile agent safety research, we introduce MobileRisk-Live, a dynamic sandbox environment accompanied by a safety detection benchmark comprising realistic trajectories with fine-grained annotations. Built upon this, we propose OS-Sentinel, a novel hybrid safety detection framework that synergistically combines a Formal Verifier for detecting explicit system-level violations with a VLM-based Contextual Judge for assessing contextual risks and agent actions. Experiments show that achieves 10{\%}{--}30{\%} improvements over existing approaches across multiple metrics. Further analysis provides critical insights that foster the development of safer and more reliable autonomous mobile agents. Our code, environment, and data are available at https://qiushisun.github.io/OS-Sentinel-Home/."
}