SafeRoute introduces an adaptive approach to LLM safety that routes potentially harmful prompts to the right-sized safety model, balancing efficiency with protection.

• Uses a small model for most inputs, preserving computational efficiency
• Selectively routes challenging cases to larger, more capable safety models
• Achieves 99.7% accuracy of large models while reducing computation by 67%
• Employs innovative uncertainty estimation to determine which model should handle each input

This research matters for security teams needing to deploy robust safety guardrails at scale without prohibitive computational costs, enabling more efficient harmful content filtering in production LLM applications.

SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language Models