Vision-Language Models (VLMs) are known to be vulnerable to adversarial attacks, where subtle perturbations to images or texts induce erroneous outputs. However, most text-based attacks are adapted from language-model-centric methods, in which the visual input is fixed during optimization, resulting in adversarial prompts that are tied to specific images and thus limiting their attack effectiveness. To this end, we first introduce a new research perspective: cross-image transferability for adversarial prompts. We then propose GhostPrompt, an adversarial prompt that is optimized once and reused to steer VLM outputs toward attacker-specified responses across diverse images. GhostPrompt employs a joint optimization that distills image-invariant adversarial features into the prompt by"worst-case"generation. Specifically, it alternates between constructing hard visual conditions for the current prompt and updating the prompt to remain effective under these conditions. Extensive experiments on prevalent VLMs verify that \ourmethod achieves an improvement of over 30% in attack success rates compared to state-of-the-art (SoTA) baselines, while reducing computation time by ~70%. Our code is avalable at https://github.com/Ye-ze-yu/GhostPrompt.
Trained on large corpora of image-text pairs, vision-language models (VLMs) have proven broadly useful across many applications. However, they can still make errors that humans rarely do, particularly when exposed to adversarial inputs crafted to mislead them. Traditional approaches to uncovering such vulnerabilities typically optimize a single input, such as a text prompt, to induce incorrect predictions while remaining plausible to human readers. These methods tend to identify only one or a few high-impact adversarial examples, offering a narrow view of model weaknesses. In contrast, we argue that a Quality-Diversity (QD) perspective is more informative. Rather than searching for a single best attack, QD explicitly aims to generate many high-quality adversarial prompts spanning diverse behaviors and characteristics. This allows us not only to diagnose model weaknesses, but also to characterize which prompts are robust and which are especially fragile. Our experiments show that CVT-MAP-Elites, a QD method integrated into our pipeline, discovers a richer and more diverse set of meaningful adversarial samples than quality-only optimization. Consequently, our approach achieves broader search-space coverage and provides deeper insight into VLM failure modes on text-to-image retrieval tasks in both general and medical domains.
Thai Huy Nguyen, Khoa Tran, Quan Minh Phan et al.· Annual Conference on Genetic...· 0 citations
This paper proposes a novel LVLM attack method, called BadPhase with further backdoor designs, to implant adversarial phase as triggers into any image inputs via data poisoning so as to control the LVLMs’ predictions and finds that LVLMs are sensitive to the phase-aware image structure.
Daizong Liu, Junhao Dong, Xiang Fang et al.· 0 citations
This paper identifies three previously overlooked issues caused by inappropriate cross-modal interactions and excessive operations in the Simple Vision-Language Attack (SimVLA) pipeline, and proposes the SimVLA, which observably improves transferability and efficiency.
Yuchen Ren, Zhengyu Zhao, Chenhao Lin et al.· IEEE Transactions on Informa...· 0 citations
A new perspective of information theory is introduced to investigate LVLMs’ transferable characteristics by exploring the relative dependence between outputs of the LVLM model and input adversarial samples and formulate the complicated calculation of information gain as an estimation problem and incorporate such informative constraints into the adversarial learning process.
Xiaowen Cai, Daizong Liu, Xiaoye Qu et al.· Neural Information Processin...· 7 citations
The recent progress in the vision-language model (VLM) research made it one of the key aspects of artificial intelligence due to joint vision and language processing capabilities required for such tasks as image retrieval, visual question answering, autonomous systems and medical image analysis. However, despite the excellent results obtained with such systems, they still appear to be extremely vulnerable to adversarial perturbations which result in a significant decline in prediction accuracy and reliability due to even subtle input transformations. The purpose of this paper is to provide a systematic method for analyzing the adversarial robustness of vision-language models under both clean and perturbed conditions. The suggested framework is based on Semantic Counterfactual Augmentation (SCA) and Curriculum Contrastive Adversarial Training (CCAT) and aims to maintain semantic consistency and increase the robustness of the model respectively. The framework is tested using a CLIP-based vision-language model on the subset of Flickr8k dataset with Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks. As a result of the experiment, the performance gap is identified between the clean and perturbed images which confirms the vulnerabilities of the conventional VLM while indicating better robustness consistency with the suggested framework.
Kumari Anjali, Karnatakam Veda Sahithi, Sakinala Jyotsna· 2026 4th International Confe...· 0 citations