SDD: Self-Degraded Defense against Malicious Fine-tuning
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912507398979584 |
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| author | Chen, Zixuan Lu, Weikai Lin, Xin Zeng, Ziqian |
| author_facet | Chen, Zixuan Lu, Weikai Lin, Xin Zeng, Ziqian |
| contents | Open-source Large Language Models (LLMs) often employ safety alignment methods to resist harmful instructions. However, recent research shows that maliciously fine-tuning these LLMs on harmful data can easily bypass these safeguards. To counter this, we theoretically uncover why malicious fine-tuning succeeds and identify potential defense strategies. Building on the theoretical analysis, we introduce the Self-Degraded Defense (SDD) framework. SDD encourages LLMs to produce high-quality but irrelevant responses to harmful prompts. When attackers attempt malicious fine-tuning, the general capability of the LLM aligned by SDD will significantly decrease, rendering it incapable of following harmful instructions. Our experimental results confirm SDD's effectiveness against such attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21182 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SDD: Self-Degraded Defense against Malicious Fine-tuning Chen, Zixuan Lu, Weikai Lin, Xin Zeng, Ziqian Cryptography and Security Artificial Intelligence Open-source Large Language Models (LLMs) often employ safety alignment methods to resist harmful instructions. However, recent research shows that maliciously fine-tuning these LLMs on harmful data can easily bypass these safeguards. To counter this, we theoretically uncover why malicious fine-tuning succeeds and identify potential defense strategies. Building on the theoretical analysis, we introduce the Self-Degraded Defense (SDD) framework. SDD encourages LLMs to produce high-quality but irrelevant responses to harmful prompts. When attackers attempt malicious fine-tuning, the general capability of the LLM aligned by SDD will significantly decrease, rendering it incapable of following harmful instructions. Our experimental results confirm SDD's effectiveness against such attacks. |
| title | SDD: Self-Degraded Defense against Malicious Fine-tuning |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2507.21182 |