@inproceedings{
huang2024vaccine,
title={Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack},
author={Tiansheng Huang and Sihao Hu and Ling Liu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lpXDZKiAnt}
}
The new paradigm of fine-tuning-as-a-service introduces a new attack surface for Large Language Models (LLMs): a few harmful data uploaded by users can easily trick the fine-tuning to produce an alignment-broken model. We conduct an empirical analysis and uncover
a \textit{harmful embedding drift} phenomenon, showing a probable 
cause of the alignment-broken effect. Inspired by our findings, we propose Vaccine, a perturbation-aware alignment technique to mitigate the security risk of users  fine-tuning. The core idea of Vaccine is to produce invariant hidden embeddings by progressively adding crafted perturbation to them in the alignment phase. This enables the embeddings to withstand harmful perturbation from  un-sanitized user data in the fine-tuning phase. Our results on open source mainstream LLMs (e.g., Llama2, Opt, Vicuna) demonstrate that Vaccine can boost the robustness of alignment against harmful prompts induced embedding drift while reserving reasoning ability towards benign prompts. Our code is available at  https://github.com/git-disl/Vaccine.
Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack
Tiansheng Huang Sihao Hu Ling Liu