SLDR: Defending Against Malicious Fine-tuning via Selective Layers Recovery and Dynamic Routing
Fine-tuning-as-a-service enables users to adapt aligned large language models (LLMs) to specialized tasks, but malicious fine-tuning can erode refusal behavior while preserving task performance on legitimate inputs. We revisit recent layer-wise safety diagnostics and find that safety sensitivity is signed: scaling different layers can strengthen refusal, weaken it, or have little effect. Motivated by this observation, we propose SLDR, a post-fine-tuning defense based on Selective Layers Recovery and Dynamic Routing. SLDR trains a LoRA recovery adapter only on the layers with the maximum and minimum sensitivity scores in the signed spectrum, and uses representation-based dynamic routing in...
| Authors | Hui Zhang, Yachao Yuan, Jiayun Wang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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