The STAIR framework enhances LLM safety through introspective reasoning, enabling models to identify risks and generate safer responses without compromising performance.

• Addresses the safety-performance tradeoff that plagues traditional approaches
• Employs multi-stage reasoning where models evaluate their own outputs before responding
• Demonstrates superior resistance to jailbreak attacks compared to conventional methods
• Maintains high performance while significantly improving safety alignment

This research is critical for security professionals as it offers a more robust approach to preventing harmful AI outputs without the vulnerabilities of direct-refusal methods, potentially reducing organizational risk from AI deployments.

STAIR: Improving Safety Alignment with Introspective Reasoning