SaRO introduces a novel framework that addresses critical weaknesses in current LLM safety alignment techniques by implementing reasoning-based safety optimization.

• Tackles under-generalization that leaves models vulnerable to novel jailbreak attacks
• Prevents over-alignment that causes excessive refusal of legitimate requests
• Uses semantic understanding to differentiate between harmful and benign prompts that appear similar in embedding space
• Demonstrates improved resistance to attacks while maintaining helpfulness on valid queries

This research is crucial for security professionals as it provides a more robust defense against evolving jailbreak techniques while preserving model utility - a critical balance for real-world deployments.

SaRO: Enhancing LLM Safety through Reasoning-based Alignment