Lifelong Safety Alignment for Language Models

TL;DR

This paper introduces a lifelong safety alignment framework for LLMs that continuously adapts to new jailbreaking strategies by training a Meta-Attacker and Defender in a competitive setup, significantly improving robustness.

Contribution

It presents a novel lifelong safety alignment framework with a Meta-Attacker and Defender, leveraging research insights to enhance LLM safety against unseen attacks.

Findings

Meta-Attacker achieves 73% attack success rate initially

Defender reduces attack success rate to 7% after training

Framework enables safer deployment of LLMs in open environments

Abstract

LLMs have made impressive progress, but their growing capabilities also expose them to highly flexible jailbreaking attacks designed to bypass safety alignment. While many existing defenses focus on known types of attacks, it is more critical to prepare LLMs for unseen attacks that may arise during deployment. To address this, we propose a lifelong safety alignment framework that enables LLMs to continuously adapt to new and evolving jailbreaking strategies. Our framework introduces a competitive setup between two components: a Meta-Attacker, trained to actively discover novel jailbreaking strategies, and a Defender, trained to resist them. To effectively warm up the Meta-Attacker, we first leverage the GPT-4o API to extract key insights from a large collection of jailbreak-related research papers. Through iterative training, the first iteration Meta-Attacker achieves a 73% attack success rate (ASR) on RR and a 57% transfer ASR on LAT using only single-turn attacks. Meanwhile, the Defender progressively improves its robustness and ultimately reduces the Meta-Attacker's success rate to just 7%, enabling safer and more reliable deployment of LLMs in open-ended environments. The code is available at https://github.com/sail-sg/LifelongSafetyAlignment.