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arXiv · 2507.21182

SDD: Self-Degraded Defense against Malicious Fine-tuning

Zixuan Chen·Weikai Lu·Xin Lin·Ziqian Zeng
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Abstract

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.

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Zixuan Chen, Weikai Lu, Xin Lin, Ziqian Zeng. 2025-07-27. SDD: Self-Degraded Defense against Malicious Fine-tuning. https://arxiv.org/abs/2507.21182

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