@inproceedings{wang-etal-2025-g,
    title = "{G}-Safeguard: A Topology-Guided Security Lens and Treatment on {LLM}-based Multi-agent Systems",
    author = "Wang, Shilong  and
      Zhang, Guibin  and
      Yu, Miao  and
      Wan, Guancheng  and
      Meng, Fanci  and
      Guo, Chongye  and
      Wang, Kun  and
      Wang, Yang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.359/",
    pages = "7261--7276",
    ISBN = "979-8-89176-251-0",
    abstract = "Large Language Model (LLM)-based Multi-agent Systems (MAS) have demonstrated remarkable capabilities in various complex tasks, ranging from collaborative problem-solving to autonomous decision-making. However, as these systems become increasingly integrated into critical applications, their vulnerability to \textit{adversarial attacks}, \textit{misinformation propagation}, and \textit{unintended behaviors} have raised significant concerns. To address this challenge, we introduce G-Safeguard, a topology-guided security lens and treatment for robust LLM-MAS, which leverages graph neural networks to detect anomalies on the multi-agent utterance graph and employ topological intervention for attack remediation. Extensive experiments demonstrate that G-Safeguard: (I) exhibits significant effectiveness under various attack strategies, recovering over 40{\%} of the performance for prompt injection; (II) is highly adaptable to diverse LLM backbones and large-scale MAS; (III) can seamlessly combine with mainstream MAS with security guarantees."
}