G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912233576988672 |
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| author | Wang, Shilong Zhang, Guibin Yu, Miao Wan, Guancheng Meng, Fanci Guo, Chongye Wang, Kun Wang, Yang |
| author_facet | Wang, Shilong Zhang, Guibin Yu, Miao Wan, Guancheng Meng, Fanci Guo, Chongye Wang, Kun Wang, Yang |
| contents | 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 adversarial attacks, misinformation propagation, and 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. The code is available at https://github.com/wslong20/G-safeguard. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11127 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems Wang, Shilong Zhang, Guibin Yu, Miao Wan, Guancheng Meng, Fanci Guo, Chongye Wang, Kun Wang, Yang Cryptography and Security Machine Learning Multiagent Systems 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 adversarial attacks, misinformation propagation, and 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. The code is available at https://github.com/wslong20/G-safeguard. |
| title | G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems |
| topic | Cryptography and Security Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2502.11127 |