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GroupGuard: A Framework for Modeling and Defending Collusive Attacks in Multi-Agent Systems

arXiv:2603.13940v1 Announce Type: new Abstract: While large language model-based agents demonstrate great potential in collaborative tasks, their interactivity also introduces security vulnerabilities. In this paper, we propose and model group collusive attacks, a highly destructive threat in which multiple agents coordinate via sociological strategies to mislead the system. To address this challenge, we introduce GroupGuard, a training-free defense framework that employs a multi-layered defense strategy, including continuous graph-based monitoring, active honeypot inducement, and structural pruning, to identify and isolate collusive agents. Experimental results across five datasets and four topologies demonstrate that group collusive attacks increase the attack success rate by up to 15\% compared to individual attacks. GroupGuard consistently achieves high detection accuracy (up to 88\%) and effectively restores collaborative performance, providing a robust solution for securing mult

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Yiling Tao, Xinran Zheng, Shuo Yang, Meiling Tao, Xingjun Wang · · 1 min read · 3 views
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arXiv:2603.13940v1 Announce Type: new Abstract: While large language model-based agents demonstrate great potential in collaborative tasks, their interactivity also introduces security vulnerabilities. In this paper, we propose and model group collusive attacks, a highly destructive threat in which multiple agents coordinate via sociological strategies to mislead the system. To address this challenge, we introduce GroupGuard, a training-free defense framework that employs a multi-layered defense strategy, including continuous graph-based monitoring, active honeypot inducement, and structural pruning, to identify and isolate collusive agents. Experimental results across five datasets and four topologies demonstrate that group collusive attacks increase the attack success rate by up to 15\% compared to individual attacks. GroupGuard consistently achieves high detection accuracy (up to 88\%) and effectively restores collaborative performance, providing a robust solution for securing multi-agent systems.

Executive Summary

This paper proposes GroupGuard, a training-free defense framework to counter group collusive attacks in multi-agent systems. GroupGuard employs a multi-layered defense strategy, including continuous graph-based monitoring, active honeypot inducement, and structural pruning, to identify and isolate collusive agents. Experimental results demonstrate GroupGuard's effectiveness in detecting group collusive attacks with high accuracy, restoring collaborative performance, and providing a robust solution for securing multi-agent systems. The paper's contribution is significant, as it addresses a critical security vulnerability in large language model-based agents. The proposed framework's training-free nature makes it an attractive solution for real-world applications.

Key Points

  • ▸ GroupGuard is a training-free defense framework to counter group collusive attacks in multi-agent systems.
  • ▸ GroupGuard employs a multi-layered defense strategy to identify and isolate collusive agents.
  • ▸ Experimental results demonstrate GroupGuard's effectiveness in detecting group collusive attacks with high accuracy.

Merits

Strength

The paper proposes a practical solution to a critical security vulnerability in multi-agent systems.

Contribution

The paper presents a novel framework that addresses a previously understudied area of security in multi-agent systems.

Demerits

Limitation

The paper assumes the presence of a reliable graph-based monitoring system, which may not be feasible in all scenarios.

Expert Commentary

While the paper makes significant contributions to the field of security in multi-agent systems, it is essential to consider the limitations and potential challenges of implementing the proposed framework in real-world scenarios. For instance, the assumption of a reliable graph-based monitoring system may not be feasible in all scenarios. Furthermore, the paper's focus on group collusive attacks may not capture other types of attacks that could compromise the security of multi-agent systems. Nevertheless, the proposed framework is a valuable addition to the field, and its potential to enhance the security of multi-agent systems is substantial.

Recommendations

  • ✓ Future research should investigate the feasibility of implementing the proposed framework in real-world scenarios, including the development of reliable graph-based monitoring systems.
  • ✓ The paper's focus on group collusive attacks should be expanded to explore other types of attacks that could compromise the security of multi-agent systems.

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