This research introduces a data-free methodology for developing effective guardrails against off-topic LLM misuse, addressing key limitations of current approaches.

Key innovations:

• Eliminates reliance on curated examples or custom classifiers that often create high false-positive rates
• Enables guardrail development without requiring real-world data unavailable in pre-production
• Provides a flexible framework adaptable to various security contexts
• Specifically targets off-topic prompt detection while maintaining system usability

Security significance: This approach advances LLM safety by enabling more robust defenses against misuse while reducing false positives that hamper legitimate use cases. The methodology allows security teams to implement effective guardrails earlier in development cycles.

A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection