{
  "id": 3820,
  "url": "https://arxiv.org/abs/2605.24550v1",
  "title": "Jailbreak to Protect: Buffering and Reinforcing via Temporary Jailbreaking for Safe Fine-Tuning in Large Language Models",
  "summary": "Fine-tuning-as-a-Service (FaaS) enables personalization of large language models (LLMs), but it can weaken safety-alignment under harmful fine-tuning attacks. Recent work has shown that activating harmful-behavior modules during fine-tuning can prevent models from learning undesired behaviors, but its mechanism remains unclear. In this paper, we revisit temporary jailbreaking as a defense against harmful fine-tuning and provide a gradient-level analysis showing that it saturates safety-degrading",
  "authors": "Seokil Ham, Jaehyuk Jang, Wonjun Lee, Changick Kim",
  "category": "research",
  "topics": "safety-alignment,military-security",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-23T12:35:02.000Z",
  "fetched_at": "2026-07-14T16:30:31.922Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3820",
  "original_url": "https://arxiv.org/abs/2605.24550v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}