@inproceedings{chen-etal-2026-jarvis,
    title = "{JARVIS} or Ultron? A Survey on the Safety and Security Threats of Computer-Using Agents",
    author = "Chen, Ada  and
      Wu, Yongjiang  and
      Zhang, Junyuan  and
      Xiao, Jingyu  and
      Yang, Shu  and
      Huang, Jen-tse  and
      Wang, Kun  and
      Wang, Wenxuan  and
      Wang, Shuai",
    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.2106/",
    pages = "45407--45441",
    ISBN = "979-8-89176-390-6",
    abstract = "Recently, AI-driven interactions with computing devices have advanced from basic prototype tools to sophisticated, LLM-based systems that emulate human-like operations in graphical user interfaces. We are now witnessing the emergence of Computer-Using Agents (CUAs), capable of autonomously performing tasks such as navigating desktop applications, web pages, and mobile apps. However, as these agents grow in capability, they also introduce novel safety and security risks. Vulnerabilities in LLM-driven reasoning, with the added complexity of integrating multiple software components and multimodal inputs, further complicate the security landscape. In this paper, we present a systematization of knowledge on the safety and security threats of CUAs. We conduct a comprehensive literature review and distill our findings along four research objectives: (i) define the CUA that suits safety analysis; (ii) categorize current safety threats among CUAs; (iii) propose a comprehensive taxonomy of existing defensive strategies; (iv) summarize prevailing benchmarks, datasets, and evaluation metrics used to assess the safety and performance of CUAs. Building on these insights, our work provides future researchers with a structured foundation for exploring unexplored vulnerabilities and offers practitioners actionable guidance in designing and deploying secure Computer-Using Agents."
}