Jailbreak to Protect: Buffering and Reinforcing via Temporary Jailbreaking for Safe Fine-Tuning in Large Language Models
The paper discusses a new framework for safe fine-tuning of large language models (LLMs) called Buffer-and-Reinforce. This framework utilizes temporary jailbreaking to mitigate harmful updates during user fine-tuning while preserving performance. The authors present experimental results demonstrating the framework's effectiveness in enhancing safety without additional safety data or significant computational costs.
- ▪Fine-tuning-as-a-Service (FaaS) can weaken safety-alignment under harmful fine-tuning attacks.
- ▪The proposed Buffer-and-Reinforce framework buffers harmful updates and reinforces safety after adaptation.
- ▪Extensive experiments show that the framework achieves superior safety and utility with minimal computational cost.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24550 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.24550 (cs) [Submitted on 23 May 2026] Title:Jailbreak to Protect: Buffering and Reinforcing via Temporary Jailbreaking for Safe Fine-Tuning in Large Language Models Authors:Seokil Ham, Jaehyuk Jang, Wonjun Lee, Changick Kim View a PDF of the paper titled Jailbreak to Protect: Buffering and Reinforcing via Temporary Jailbreaking for Safe Fine-Tuning in Large Language Models, by Seokil Ham and 3 other authors View PDF HTML (experimental) Abstract:Fine-tuning-as-a-Service (FaaS) enables personalization of large language models (LLMs), but it can weaken safety-alignment under harmful fine-tuning attacks.
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