Lifelong Safety Alignment for Language Models
Authors: name: Haoyu Wang, name: Zeyu Qin, name: Yifei Zhao, name: Chao Du, name: Min Lin, name: Xueqian Wang, name: Tianyu Pang

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.