MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debate

Investigating Adversarial Attacks in Collaborative LLMs Through Debate

The paper "MultiAgent Collaboration Attack: Investigating Adversarial Attacks in Large Language Model Collaborations via Debate" focuses on the vulnerabilities and robustness of multi-agent systems composed of LLMs when subjected to adversarial influence. In particular, it examines how adversarial agents can disrupt collaborative efforts through persuasive debate.

LLMs have illustrated notable performance across diverse tasks, managing challenges in reasoning, code generation, and complex problem-solving domains. The integration of LLMs as agents that can collaborate with each other to perform intricate real-world tasks marks a significant milestone. However, the increased complexity and interconnectedness inherently introduce risks, particularly when agents from different authorities interact. This study is pivotal in identifying and addressing the robustness and susceptibility of these collaborative systems to adversarial attacks.

Key Contributions

Debate-based Adversarial Framework:

• The research introduces a framework where multiple LLMs engage in a debate to collaboratively solve tasks, mimicking a human-like interaction model.
• Four representative tasks are employed: reasoning (MMLU), trustworthiness (TruthfulQA), medical (MedMCQA), and legal (Scalr from LegalBench). These tasks include domain-specific challenges and high-risk applications, creating a comprehensive evaluation scenario.

Metrics for Adversarial Effectiveness:

• Introduces metrics for system accuracy and model agreement to assess the influence of an adversary.
• A specific focus is placed on measuring the persuasive power of adversarial agents by evaluating how well they can convince other agents to accept an incorrect answer.

Experimental Results:

• Results indicate that collaborative debates among agents are vulnerable to adversarial attacks, with system accuracy dropping by up to 40%, and individual accuracy of group models decreasing by up to 30%.
• The research underscores the criticality of a model's persuasive power, showing how adversarial agents can leverage it to sway the debate outcome.

Inference-time Argument Optimization:

• Proposes strategies such as generating multiple arguments (Best-of-N) and leveraging additional context to enhance the persuasion ability of adversarial models during inference.
• Empirical results show improvements in the adversary's ability to degrade performance further when optimized strategies are employed.

Ablation Studies:

• The study examines the effects of varying the number of debate rounds and agents, revealing that increasing either parameter does not necessarily mitigate the adversarial influence.
• Concludes that the primary driver of successful attacks is the agent's inherent persuasive ability rather than the structure of the collaborative process.

Mitigation Strategies:

• Proposes a prompt-based alert to warn agents of potential adversaries, though results show that this method is not universally effective.
• Highlights the need for more sophisticated defense mechanisms to ensure robust multi-agent collaborations, especially in contexts where agents operate independently and interact with external agents.

Theoretical and Practical Implications

The findings have significant implications both theoretically and practically:

Theoretical:

• Introduces a new perspective on studying adversarial attacks in collaborative systems, focusing on the role of persuasion.
• Provides a robust framework and metrics for future research on multi-agent collaborations in AI.

Practical:

• Raises critical awareness regarding the deployment of LLMs in collaborative, real-world applications where robustness against adversarial attacks is essential.
• Suggests potential vulnerabilities in systems utilizing LLM collaborations, prompting the development of improved defensive strategies.

Future Directions

Given the fast-paced development and deployment of LLMs, future work could explore:

• Enhanced defensive strategies incorporating machine learning techniques to detect and mitigate persuasive adversarial behaviors.
• Alternative collaborative protocols that inherently reduce vulnerability to persuasion-based attacks.
• Expansion of the evaluation framework to include a wider range of tasks and diverse model architectures.

In summary, this paper makes significant strides in understanding and improving the robustness of multi-agent systems composed of LLMs against adversarial attacks through debate. Identifying the critical role of persuasive abilities in adversarial success, it provides a foundational basis for developing more secure and reliable collaborative AI systems.