RepBend is a new technique that modifies internal representations within large language models to enhance safety without compromising performance.

• Addresses critical safety risks in LLMs including harmful content generation and vulnerability to adversarial attacks
• Operates by bending the representation space to prevent harmful outputs while preserving model functionality
• Demonstrates effectiveness across multiple popular LLM architectures
• Offers a more robust safety solution compared to traditional fine-tuning approaches

This research is crucial for security as it provides a fundamental approach to hardening LLMs against jailbreak attempts and harmful use cases, essential for deploying these models in high-stakes environments.

Representation Bending for Large Language Model Safety