Despite moral alignment training, LLMs remain susceptible to jailbreak attacks. The paper proposes AutoDefense, a multi-agent defense strategy ensuring LLMs do not generate harmful information, even when provoked. The framework enhances performance for both harmful and safe prompts, with all related resources available on GitHub.

• Distributes defense tasks among multiple LLM agents.
• Demonstrates increased robustness and instruction-following.
• Allows for flexible integration with various LLM sizes and types.

This is a significant contribution to creating safer AI environments. Implementing such defense mechanisms is crucial as it helps maintain ethical AI standards and builds trust in LLMs’ ability to handle sensitive content responsibly.