Recovering the Pre-Fine-Tuning Weights of Generative Models
<|reference_start|>Recovering the Pre-Fine-Tuning Weights of Generative Models: The dominant paradigm in generative modeling consists of two steps: i) pre-training on a large-scale but unsafe dataset, ii) aligning the pre-trained model with human values via fine-tuning. This practice is considered safe, as no current method can recover the unsafe, pre-fine-tuning model weights. In this paper, we demonstrate that this assumption is often false. Concretely, we present Spectral DeTuning, a method that can recover the weights of the pre-fine-tuning model using a few low-rank (LoRA) fine-tuned models. In contrast to previous attacks that attempt to recover pre-fine-tuning capabilities, our method aims to recover the exact pre-fine-tuning weights. Our approach exploits this new vulnerability against large-scale models such as a personalized Stable Diffusion and an aligned Mistral.<|reference_end|>
@article{horwitz2024recovering,
    title={Recovering the Pre-Fine-Tuning Weights of Generative Models},
    author={Eliahu Horwitz, Jonathan Kahana, Yedid Hoshen},
    journal={arXiv preprint arXiv:2402.10208},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10208},
    primaryClass={cs.LG cs.CL cs.CR cs.CV}
}