Researchers have identified a vulnerability in Large Language Models (LLMs), known as jailbreak attacks, where users may solicit harmful information. To combat this, the AutoDefense framework introduces a response-filtering system that employs multiple LLM agents in different roles to enhance security.

• Multi-agent defense mechanism against jailbreak attacks in LLMs.
• Division of responsibilities among LLM agents.
• Framework compatible with various sizes and types of LLMs.
• Extensive testing confirms effectiveness in maintaining robustness and performance.
• Open-source code and data available for community use.

The significance of this paper lies in its potential to enhance LLM user safety and its adaptability across different LLM architectures. The approach could potentially inform further advancements in secure AI deployments in sensitive applications.