This research introduces a novel feature-aware method for harmful response rejection (FMM) that enhances LLM security by detecting malicious content in the model's feature space.

• Identifies harmful content by analyzing feature representations rather than just text outputs
• Implements an adaptive intervention mechanism to prevent harmful responses
• Demonstrates superior performance over existing defense methods against jailbreak attacks
• Provides a practical approach that can be integrated with existing LLM systems

This advancement is crucial for improving LLM safety in production environments where malicious users may attempt to extract harmful content through sophisticated prompting techniques.

Feature-Aware Malicious Output Detection and Mitigation