  In the realm of large vision language models (LVLMs), jailbreak attacks serve
as a red-teaming approach to bypass guardrails and uncover safety implications.
Existing jailbreaks predominantly focus on the visual modality, perturbing
solely visual inputs in the prompt for attacks. However, they fall short when
confronted with aligned models that fuse visual and textual features
simultaneously for generation. To address this limitation, this paper
introduces the Bi-Modal Adversarial Prompt Attack (BAP), which executes
jailbreaks by optimizing textual and visual prompts cohesively. Initially, we
adversarially embed universally harmful perturbations in an image, guided by a
few-shot query-agnostic corpus (e.g., affirmative prefixes and negative
inhibitions). This process ensures that image prompt LVLMs to respond
positively to any harmful queries. Subsequently, leveraging the adversarial
image, we optimize textual prompts with specific harmful intent. In particular,
we utilize a large language model to analyze jailbreak failures and employ
chain-of-thought reasoning to refine textual prompts through a
feedback-iteration manner. To validate the efficacy of our approach, we
conducted extensive evaluations on various datasets and LVLMs, demonstrating
that our method significantly outperforms other methods by large margins
(+29.03% in attack success rate on average). Additionally, we showcase the
potential of our attacks on black-box commercial LVLMs, such as Gemini and
ChatGLM.
