Large vision-language models (VLMs) such as GPT-4 have achieved unprecedented
performance in response generation, especially with visual inputs, enabling
more creative and adaptable interaction than large language models such as
ChatGPT. Nonetheless, multimodal generation exacerbates safety concerns, since
adversaries may successfully evade the entire system by subtly manipulating the
most vulnerable modality (e.g., vision). To this end, we propose evaluating the
robustness of open-source large VLMs in the most realistic and high-risk
setting, where adversaries have only black-box system access and seek to
deceive the model into returning the targeted responses. In particular, we
first craft targeted adversarial examples against pretrained models such as
CLIP and BLIP, and then transfer these adversarial examples to other VLMs such
as MiniGPT-4, LLaVA, UniDiffuser, BLIP-2, and Img2Prompt. In addition, we
observe that black-box queries on these VLMs can further improve the
effectiveness of targeted evasion, resulting in a surprisingly high success
rate for generating targeted responses. Our findings provide a quantitative
understanding regarding the adversarial vulnerability of large VLMs and call
for a more thorough examination of their potential security flaws before
deployment in practice. Code is at this https URL

本研究评估了开源大型视觉 - 语言模型的鲁棒性，发现黑盒查询可以进一步提高定向逃避的效果。研究结果为大型视觉 - 语言模型的敌对脆弱性提供了量化的理解，并呼吁在实际部署之前对它们的潜在安全漏洞进行更全面的研究。