Recent advancements in Large Vision-Language Models (VLMs) have underscored
their superiority in various multimodal tasks. However, the adversarial
robustness of VLMs has not been fully explored. Existing methods mainly assess
robustness through unimodal adversarial attacks that perturb images, while
assuming inherent resilience against text-based attacks. Different from
existing attacks, in this work we propose a more comprehensive strategy that
jointly attacks both text and image modalities to exploit a broader spectrum of
vulnerability within VLMs. Specifically, we propose a dual optimization
objective aimed at guiding the model to generate affirmative responses with
high toxicity. Our attack method begins by optimizing an adversarial image
prefix from random noise to generate diverse harmful responses in the absence
of text input, thus imbuing the image with toxic semantics. Subsequently, an
adversarial text suffix is integrated and co-optimized with the adversarial
image prefix to maximize the probability of eliciting affirmative responses to
various harmful instructions. The discovered adversarial image prefix and text
suffix are collectively denoted as a Universal Master Key (UMK). When
integrated into various malicious queries, UMK can circumvent the alignment
defenses of VLMs and lead to the generation of objectionable content, known as
jailbreaks. The experimental results demonstrate that our universal attack
strategy can effectively jailbreak MiniGPT-4 with a 96% success rate,
highlighting the vulnerability of VLMs and the urgent need for new alignment
strategies.

通过对大规模视觉语言模型的攻击，我们提出了一种综合性的策略，该策略同时攻击文本和图像模态，以利用视觉语言模型内的更广泛的脆弱性。我们的实验结果表明，我们的通用攻击策略可以有效地越狱 MiniGPT-4，成功率达到 96％，突显了视觉语言模型的脆弱性和对新的对齐策略的迫切需求。