title: Lifelong Safety Alignment for Language Models
summary: 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.
paper_authors._id: 6835346b2fdc5f8e8ea1e3cf
paper_authors.name: Haoyu Wang
paper_authors._id: 6835346b2fdc5f8e8ea1e3d0
paper_authors.name: Zeyu Qin
paper_authors._id: 6835346b2fdc5f8e8ea1e3d1
paper_authors.name: Yifei Zhao
paper_authors._id: 6835346b2fdc5f8e8ea1e3d2
paper_authors.name: Chao Du
paper_authors._id: 6835346b2fdc5f8e8ea1e3d3
paper_authors.name: Min Lin
paper_authors._id: 6835346b2fdc5f8e8ea1e3d4
paper_authors.name: Xueqian Wang
paper_authors._id: 6835346b2fdc5f8e8ea1e3d5
paper_authors.name: Tianyu Pang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T12:31:57.554Z
paper_authors._id: 63d91b6d255ef6add20e1b38
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1675921369867-63d91b6d255ef6add20e1b38.jpeg
paper_authors.fullname: Tianyu Pang
paper_authors.type: user
paper_authors.user: P2333
paper_title: Lifelong Safety Alignment for Language Models
paper_summary: 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.
paper_ai_summary: A lifecycle safety alignment framework employs a Meta-Attacker and Defender to adapt LLMs to novel jailbreaking strategies, improving robustness in deployment.
paper_ai_keywords: LLMs jailbreaking attacks safety alignment lifelong safety alignment framework Meta-Attacker Defender GPT-4o API attack success rate transfer attack success rate single-turn attacks