@inproceedings{zhan-etal-2025-adaptive,
    title = "Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on {LLM} Agents",
    author = "Zhan, Qiusi  and
      Fang, Richard  and
      Panchal, Henil Shalin  and
      Kang, Daniel",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.395/",
    pages = "7101--7117",
    ISBN = "979-8-89176-195-7",
    abstract = "Large Language Model (LLM) agents exhibit remarkable performance across diverse applications by using external tools to interact with environments. However, integrating external tools introduces security risks, such as indirect prompt injection (IPI) attacks. Despite defenses designed for IPI attacks, their robustness remains questionable due to insufficient testing against adaptive attacks.In this paper, we evaluate eight different defenses and bypass all of them using adaptive attacks, consistently achieving an attack success rate of over 50{\%}.This reveals critical vulnerabilities in current defenses. Our research underscores the need for adaptive attack evaluation when designing defenses to ensure robustness and reliability.The code is available at https://github.com/uiuc-kang-lab/AdaptiveAttackAgent."
}