NLSR: Neuron-Level Safety Realignment of Large Language Models Against Harmful Fine-Tuning
Xin Yi, Shunfan Zheng, Linlin Wang, Gerard de Melo, Xiaoling Wang, Liang He
Abstract
The emergence of fine-tuning-as-a-service has revealed a new vulnerability in large language models (LLMs). A mere handful of malicious data uploaded by users can subtly manipulate the fine-tuning process, leading to a compromised alignment state. Existing methods to counteract fine-tuning attacks typically require substantial computational resources. Even with parameter-efficient techniques like LoRA, gradient updates remain essential. To address these challenges, we propose Neuron-Level Safety Realignment (NLSR), a training-free framework that restores the safety of LLMs based on the similarity difference of safety-critical neurons before and after fine-tuning. The core of our framework is first to construct a safety reference model from an initially aligned model to amplify safety-related features in neurons. We then utilize this reference model to identify safety-critical neurons, which we prepare as patches. Finally, we selectively restore only those neurons that exhibit significant similarity differences by transplanting these prepared patches, thereby minimally altering the fine-tuned model. Extensive experiments demonstrate significant safety enhancements in fine-tuned models across multiple downstream tasks, while greatly maintaining task-level accuracy. Our findings indicate that safety-critical neurons exhibit significant regional variations after fine-tuning, which can be effectively corrected through neuron transplantation from the reference model without the need for additional training.
BibTeX
@article{Yi_Zheng_Wang_de Melo_Wang_He_2025, title={NLSR: Neuron-Level Safety Realignment of Large Language Models Against Harmful Fine-Tuning}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/34762}, DOI={10.1609/aaai.v39i24.34762}, abstractNote={The emergence of fine-tuning-as-a-service has revealed a new vulnerability in large language models (LLMs). A mere handful of malicious data uploaded by users can subtly manipulate the fine-tuning process, leading to a compromised alignment state. Existing methods to counteract fine-tuning attacks typically require substantial computational resources. Even with parameter-efficient techniques like LoRA, gradient updates remain essential. To address these challenges, we propose Neuron-Level Safety Realignment (NLSR), a training-free framework that restores the safety of LLMs based on the similarity difference of safety-critical neurons before and after fine-tuning. The core of our framework is first to construct a safety reference model from an initially aligned model to amplify safety-related features in neurons. We then utilize this reference model to identify safety-critical neurons, which we prepare as patches. Finally, we selectively restore only those neurons that exhibit significant similarity differences by transplanting these prepared patches, thereby minimally altering the fine-tuned model. Extensive experiments demonstrate significant safety enhancements in fine-tuned models across multiple downstream tasks, while greatly maintaining task-level accuracy. Our findings indicate that safety-critical neurons exhibit significant regional variations after fine-tuning, which can be effectively corrected through neuron transplantation from the reference model without the need for additional training.}, number={24}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Yi, Xin and Zheng, Shunfan and Wang, Linlin and de Melo, Gerard and Wang, Xiaoling and He, Liang}, year={2025}, month={Apr.}, pages={25706-25714} }