XSPA用X形稀疏扰动攻击视觉语言模型，实现跨任务高成功率攻击。

Vision-language models (VLMs) share visual-textual representations across zero-shot classification, image captioning, and visual question answering (VQA), creating a pathway through which subtle perturbations may cause failures across tasks. We introduce X-shaped Sparse Pixel Attack (XSPA), a structured attack that restricts perturbations to two intersecting diagonal lines. Within this fixed support, XSPA jointly optimizes a classification objective, cross-task semantic guidance, perturbation magnitude, and linewise smoothness. Under the default setting, it changes about 1.04\% of image pixels. On COCO, XSPA reduces zero-shot accuracy by 52.33 points on OpenAI CLIP ViT-L/14 and 67.00 points on OpenCLIP ViT-B/16; GPT-4-based caption consistency and VQA correctness decrease by up to 58.60 and 44.25 points, respectively. Matched-budget experiments show that an adaptive saliency Top-k support achieves higher attack success than the fixed X-shaped support, while XSPA provides better SSIM and LPIPS but worse PSNR and MAE. Same-image analysis finds no three-task targeted success, indicating that strong surrogate classification effects do not reliably translate into targeted output-space failures. These results position XSPA as a controlled stress test for studying how sparse fixed geometry affects VLM robustness, rather than as a universally superior attack.