  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.
