This research introduces a novel approach to LLM safety mechanisms that are both more effective and less restrictive than current solutions.

• Addresses inadequate defenses in specialized domains like chemistry where harmful content might slip through
• Solves the problem of over-defensiveness that hampers LLM utility and responsiveness
• Proposes dynamic guided safeguards that adapt to different domains and contexts
• Demonstrates improved security without compromising model performance

This advancement is crucial for security professionals deploying LLMs in sensitive environments, offering protection against jailbreak attacks while maintaining functionality across various application domains.

Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models