Vision transformers (ViT) are deep learning architecture that uses the self-attention mechanism of transformers to analyze and process visual data, such as images. This process involves dividing an input image into smaller patches, which allows each patch to focus on the other. This approach enables high efficiency in a wide range of computer vision applications. Similar to other deep learning models, the ViT model is also sensitive to the color distribution present in images. Color and contrast variations could affect how well the model generalizes to datasets or real-world situations. This research studies the impact of the Color Channel Perturbation (CCP) Attack on Vision Transformer models. In CCP attacks, we get a new transformed dataset used for testing. We conduct the experiments on three well-known datasets, CIFAR10, Caltech256, and TinyImageNet, commonly used in computer vision for image classification tasks. We use three state-of-the-art vision transformer architectures, namely ViT, Pooling-based Vision Transformer (PiT), and Class-Attention in Image Transformers (CaiT). The result shows that the CCP attack is able to fool the vision transformer models heavily on the Caltech256 and TinyImageNet datasets. This study shows a need to investigate the defense mechanism for vision transformer models against CCP attack.