AsFT: Anchoring Safety During LLM Fine-Tuning Within Narrow Safety Basin
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911357225402368 |
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| author | Yang, Shuo Zhang, Qihui Liu, Yuyang Jia, Xiaojun Ning, Kunpeng Yao, Jiayu Wang, Jigang Dai, Hailiang Song, Yibing Yuan, Li |
| author_facet | Yang, Shuo Zhang, Qihui Liu, Yuyang Jia, Xiaojun Ning, Kunpeng Yao, Jiayu Wang, Jigang Dai, Hailiang Song, Yibing Yuan, Li |
| contents | Fine-tuning large language models (LLMs) improves performance but introduces critical safety vulnerabilities: even minimal harmful data can severely compromise safety measures. We observe that perturbations orthogonal to the alignment direction - defined by weight differences between aligned (safe) and unaligned models - rapidly compromise model safety. In contrast, updates along the alignment direction largely preserve it, revealing the parameter space as a "narrow safety basin". To address this, we propose AsFT (Anchoring Safety in Fine-Tuning) to maintain safety by explicitly constraining update directions during fine-tuning. By penalizing updates orthogonal to the alignment direction, AsFT effectively constrains the model within the "narrow safety basin," thus preserving its inherent safety. Extensive experiments on multiple datasets and models show that AsFT reduces harmful behaviors by up to 7.60%, improves task performance by 3.44%, and consistently outperforms existing methods across multiple tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08473 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AsFT: Anchoring Safety During LLM Fine-Tuning Within Narrow Safety Basin Yang, Shuo Zhang, Qihui Liu, Yuyang Jia, Xiaojun Ning, Kunpeng Yao, Jiayu Wang, Jigang Dai, Hailiang Song, Yibing Yuan, Li Machine Learning Fine-tuning large language models (LLMs) improves performance but introduces critical safety vulnerabilities: even minimal harmful data can severely compromise safety measures. We observe that perturbations orthogonal to the alignment direction - defined by weight differences between aligned (safe) and unaligned models - rapidly compromise model safety. In contrast, updates along the alignment direction largely preserve it, revealing the parameter space as a "narrow safety basin". To address this, we propose AsFT (Anchoring Safety in Fine-Tuning) to maintain safety by explicitly constraining update directions during fine-tuning. By penalizing updates orthogonal to the alignment direction, AsFT effectively constrains the model within the "narrow safety basin," thus preserving its inherent safety. Extensive experiments on multiple datasets and models show that AsFT reduces harmful behaviors by up to 7.60%, improves task performance by 3.44%, and consistently outperforms existing methods across multiple tasks. |
| title | AsFT: Anchoring Safety During LLM Fine-Tuning Within Narrow Safety Basin |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.08473 |