This research introduces a novel approach to AI safety that monitors internal activations within large language models to detect and prevent harmful outputs while preserving model capabilities.

• Leverages System 2 thinking principles from cognitive science to regulate LLM behavior
• Identifies specific neural activation patterns that correspond to unsafe content generation
• Achieves 95.7% effectiveness in preventing harmful outputs while maintaining model utility
• Demonstrates a more targeted approach than existing safety measures that often cause overcautious behavior

For security teams, this framework offers a promising path to implement more nuanced safety controls that don't compromise LLM performance in legitimate use cases.

Internal Activation as the Polar Star for Steering Unsafe LLM Behavior