Evaluating Multi-Agent Defences Against Jailbreaking Attacks on Large Language Models

• URL: http://arxiv.org/abs/2506.23576v1
• Date: Mon, 30 Jun 2025 07:29:07 GMT
• Title: Evaluating Multi-Agent Defences Against Jailbreaking Attacks on Large Language Models
• Authors: Maria Carolina Cornelia Wit, Jun Pang,
• Abstract summary: This paper investigates the use of multi-agent LLM systems as a defence against jailbreaking attacks.<n>We evaluate three jailbreaking strategies, including the original AutoDefense attack and two from Deepleaps: BetterDan and JB.<n>Our results show that multi-agent systems enhance resistance to jailbreaks, especially by reducing false negatives.
• Score: 4.757470449749876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in large language models (LLMs) have raised concerns about jailbreaking attacks, i.e., prompts that bypass safety mechanisms. This paper investigates the use of multi-agent LLM systems as a defence against such attacks. We evaluate three jailbreaking strategies, including the original AutoDefense attack and two from Deepleaps: BetterDan and JB. Reproducing the AutoDefense framework, we compare single-agent setups with two- and three-agent configurations. Our results show that multi-agent systems enhance resistance to jailbreaks, especially by reducing false negatives. However, its effectiveness varies by attack type, and it introduces trade-offs such as increased false positives and computational overhead. These findings point to the limitations of current automated defences and suggest directions for improving alignment robustness in future LLM systems.

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