Eraser: Jailbreaking Defense in Large Language Models via Unlearning
  Harmful Knowledge
Weikai Lu Ziqian Zeng Jianwei Wang Zhengdong Lu Zelin Chen Huiping Zhuang Cen Chen
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. The training of Erase does not
actually require the model's own harmful knowledge, and it can benefit from
unlearning general answers related to harmful queries, which means it does not
need assistance from the red team. The experimental results show that Eraser
can significantly reduce the jailbreaking success rate for various attacks
without compromising the general capabilities of the model.