Reasoning-to-Defend (R2D) is a novel training approach that leverages an LLM's reasoning capabilities to detect and defend against malicious jailbreak attempts.

• Integrates safety reflections directly into the LLM's generation process
• Creates a safety-aware reasoning mechanism that operates proactively
• Demonstrates significant improvements in robustness against adversarial attacks
• Offers a more efficient alternative to existing safety measures

This research addresses critical security vulnerabilities in LLMs by teaching models to reason about potential harm before responding, rather than relying solely on external filters or guardrails. Enterprise AI deployments can benefit from this approach to reduce security risks without sacrificing performance.

Reasoning-to-Defend: Safety-Aware Reasoning Can Defend Large Language Models from Jailbreaking