Lifelong Safety Alignment for Language Models

Haoyu Wang, Yifei Zhao, Zeyu Qin, Chao Du, Min Lin, Xueqian Wang, Tianyu Pang

Keywords: Safety Alignment, Language Models

TL;DR: We propose a lifelong safety alignment framework where a Meta-Attacker and Defender co-evolve to uncover and defend against unseen jailbreaking strategies.

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.

Primary Area: Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Submission Number: 11402