Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack

Tiansheng Huang, Sihao Hu, Ling Liu

Keywords: Larger language model, safety alignment, perturbation-aware alignment, harmful finetuning attack

TL;DR: We propose Vaccine, a perturbation-aware alignment solution for large language model against harmful fine-tuning attack.

Abstract:

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.

Primary Area: Safety in machine learning

Submission Number: 2807