When Allies Turn Foes: Exploring Group Characteristics of LLM -Based Multi-Agent Collaborative Systems Under Adversarial Attacks (2025)

Abstract: This paper investigates the group characteristics in multi-agent collaborative systems under adversarial attacks. Adversarial agents are tasked with generating counterfactual answers to a given collaborative problem, while collaborative agents normally interact with other agents to solve the given problem. To simulate real-world collaboration scenarios as closely as possible, we evaluate the collaborative system in three different collaboration scenarios and design three different communication strategies and different group structures. Furthermore, we explored several methods to mitigate adversarial attacks, all of which have been proven effective through our experiments. To quantify the robustness of collaborative systems against such attacks, a novel metric, System Defense Index (SDI), is introduced. Finally, we conducted an in-depth analysis from the perspective of group dynamics on how adversarial agents affect multi-agent collaborative systems, which reveals similarities between the agent collaboration process and human collaboration process. The code will be made available after publication.

## introduction in recent years, large language models (llms) have shown impressive performance on generation tasks such as reasoning @xcite , question answering @xcite ), text translation @xcite . however, they still suffer from hallucination @xcite , i.e., the generation of false or incorrect statements. to mitigate the above phenomenon, existing research has drawn inspiration from the theory of society of mind @xcite to explore llmbased multi-agent collaboration , research has been initiated based on llm-based multi-agent collaboration @xcite @xcite @xcite @xcite , which has been shown to significantly improve the factuality and reasoning accuracy of llms. it is noteworthy that the collaboration among weaker models can match or even surpass the performance of state-of-the-art models on specific datasets @xcite @xcite . while much of the current research has focused on improving the performance of multi-agent collaborative systems, relatively little attention has been paid to the impact of adversarial attacks on multiagent collaborative systems. amayuelas et al. investigate the impact of adversarial attacks on the multi-agent debate system by introducing an adversarial agent. @xcite quantitatively analyze the robustness of different collaborative systems architectures under adversarial attacks. however, the extent to which adversarial agents can influence collaborative agents, and the ways in which they affect these agents, remains unclear. moreover, existing research has not revealed how adversarial agents affect collaborative systems in terms of group dynamics. group characteristics refers to the behavioral patterns, dynamics, and interactions exhibited by group members (such as collaborative agents and adversarial agents) in a multi-agent collaborative system, which influence the overall performance and robustness of the system against adversarial attacks. understanding of the group characteristics of multi-agent collaborative systems under adversarial attacks is conducive to the advancement of knowledge regarding the degree of consistency between multi-agent collaboration and human collaboration. furthermore, it lays the groundwork for the designing more robust collaborative systems. this paper, therefore, focuses on the group characteristics of multi-agent collaborative systems under adversarial attacks. we introduce a novel metric, system defense index (sdi), to measure the defensive capabilities exhibited by a multi-agent collaborative systems. in addition, we further analyze how adversarial attacks affect collaborative systems from the perspective of group dynamics. for more details on the related work, refer to the appendix d. the contributions of this work are summarized as follows: ## experimental framework in this section, we provide a comprehensive description of the experimental framework and setup. ## analysis our experiments are primarily driven by the following research queries: (rq1) how adversarial attacks affect multi-agent collaborative systems? (rq2) what strategies mitigate adversarial attacks? (rq3) in which collaboration scenarios are multiagent collaborative systems more vulnerable to adversarial attacks? ## further analysis: a group dynamics perspective in this section, we go a step further and analyze the reasons for the success and failure of adversarial attacks in a group dynamics perspective and how such attacks affect individual behavior. ## conclusion and future work this study reveals what group characteristics multiagent collaborative systems exhibit under adversarial attacks. to quantify the performance of collaborative systems under adversarial attacks, a novel metric sdi, is introduced. this artificially introduced adversarial attack can be modeled to simulate the disagreement phenomenon that occurs in real-world multi-agent collaboration scenarios, which is important for studying how individual be-havior affects the team's final decision. our study provides a basic framework for studying the effects of such perturbations on groups. future work should focus on adversarial attacks in more realistic scenarios, such as rumor propagation, fake news dissemination, and the effect of misperception on decision-making within groups.