Recovering the Pre-Fine-Tuning Weights of Generative Models
Eliahu Horwitz Jonathan Kahana Yedid Hoshen
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.