Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large
  Language Models
Yongshuo Zong Ondrej Bohdal Tingyang Yu Yongxin Yang Timothy Hospedales
Current vision large language models (VLLMs) exhibit remarkable capabilities
yet are prone to generate harmful content and are vulnerable to even the
simplest jailbreaking attacks. Our initial analysis finds that this is due to
the presence of harmful data during vision-language instruction fine-tuning,
and that VLLM fine-tuning can cause forgetting of safety alignment previously
learned by the underpinning LLM. To address this issue, we first curate a
vision-language safe instruction-following dataset VLGuard covering various
harmful categories. Our experiments demonstrate that integrating this dataset
into standard vision-language fine-tuning or utilizing it for post-hoc
fine-tuning effectively safety aligns VLLMs. This alignment is achieved with
minimal impact on, or even enhancement of, the models' helpfulness. The
versatility of our safety fine-tuning dataset makes it a valuable resource for
safety-testing existing VLLMs, training new models or safeguarding pre-trained
VLLMs. Empirical results demonstrate that fine-tuned VLLMs effectively reject
unsafe instructions and substantially reduce the success rates of several
black-box adversarial attacks, which approach zero in many cases. The code and
dataset are available at https://github.com/ys-zong/VLGuard.