Jul 2026· Annual Conference on Genetic and Evolutionary Computation· 0 citations· 39 references
Computer Science
Abstract
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.
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.
Pre-trained Vision-Language Models (VLMs) such as CLIP achieve strong zero-shot generalization, but their performance degrades sharply under adversarial perturbations. Existing test-time adaptation methods typically rely on sample-level confidence heuristics, overlooking the intrinsic distributional structure of the data. This sample-centric approach limits robustness, as it fails to distinguish confident adversarial mispredictions from true semantic consistency. In this work, we observe that adversarial distortion is structurally brittle: while holistic representations are corrupted, semantic integrity is often preserved in the distribution of augmented views. Motivated by this insight, we propose RITA, a Robust test-tIme prompt-TAdaptation framework that shifts from sample-level estimates to distribution-level alignment. Specifically, RITA employs optimal transport to align the distribution of augmented visual features with textual prototypes, mitigating adversarial outliers and rectifying cross-modal semantic misalignment. Furthermore, we introduce a dynamic cache to progressively accumulate reliable cues from the test stream for online refinement. Extensive experiments demonstrate that RITA significantly improves adversarial robustness without compromising clean accuracy.
Xingyu Zhu, Huanshen Wu, Shuo Wang et al.· 1 citation
While Large Vision-Language Models (LVLMs), represented by LLaVA and GPT-4V, have demonstrated remarkable capabilities, their visual inputs remain vulnerable to adversarial attacks, posing significant security risks. Existing defense methods predominantly target single-task scenarios (e.g., zero-shot classification) and consequently lack generalizability across various multimodal tasks. To address this limitation, we propose a dual adversarial fine-tuning framework that jointly optimizes visual and semantic supervision signals from two modalities, enhancing model robustness while generalizing across multiple downstream tasks. The proposed framework comprises two core components, i.e., $\textbf{Visual}$ supervision branch and $\textbf{Semantic}$ supervision branch. The former branch leverages features from clean images, extracted via a frozen original vision encoder, to guide adversarial robustness while the latter incorporates caption-image alignment as a contextual signal to preserve semantic coherence under attack. Moreover, our method achieves cross-task robustness by simply replacing the CLIP vision encoder in the original model, with no need of separate task-specific retraining or architecture modifications.Extensive experiments demonstrate that our approach outperforms the state-of-the-art method in adversarial robustness evaluation across zero-shot classification, image captioning, and visual question answering (VQA) tasks.
Sibo Wang, Jie Zhang, Shiguang Shan et al.· 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
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
Vision-language models (VLMs), such as CLIP, are vulnerable to adversarial attacks, posing a serious problem for real-life applications and deployment. Adversarial fine-tuning emerges as a prominent defense method; however, different fine-tuning strategies often produce specialized models with distinct robustness characteristics. Each fine-tuned model in turn thrives in some evaluation settings but falters on others, limiting their defensive capabilities. We refer to these specialized fine-tuned models as robust model experts and propose a collaborative adversarial fine-tuning framework: CARE - Collaborative Adversarial Robustness fine-tuning using Embedding alignment. CARE maintains multiple experts during training, enables knowledge exchange through embedding-space harmonization, and consolidates the learned knowledge into a single unified robust model. Experts benefit from one another while preserving their individual specializations, enabling the final model to inherit complementary robustness properties. In this paper, we demonstrate CARE on two different adversarial fine-tuning strategies with complementary robustness behaviors. Extensive experiments on classic image classification and downstream vision-language tasks display the effectiveness of our approach, with CARE being able to outperform individually learned model experts. The results suggest that collaborative learning across model experts is a promising direction for improving adversarial robustness.
Nguyen Duc Thai, Junhao Dong, Sua Qi Rong et al.· 0 citations