Eraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge

Understand

Jailbreaking attacks can enable Large Language Models (LLMs) to bypass the safeguard and generate harmful content.

• Existing jailbreaking defense methods have failed to address the fundamental issue that harmful knowledge resides within the model, leading to potential jailbreak risks for LLMs.
• In this paper, we propose a novel defense method called Eraser, which mainly includes three goals: unlearning harmful knowledge, retaining general knowledge, and maintaining safety alignment.
• The intuition is that if an LLM forgets the specific knowledge required to answer a harmful question, it will no longer have the ability to answer harmful questions.