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Pre-trained Vision-Language Models (VLMs) like CLIP, have demonstrated strong zero-shot generalization capabilities. Despite their effectiveness on various downstream tasks, they remain vulnerable to adversarial samples. Existing methods fine-tune VLMs to improve their performance via performing adversarial training on a certain dataset. However, this can lead to model overfitting and is not a true zero-shot scenario. In this paper, we propose a truly zero-shot and training-free approach that can significantly improve the VLM's zero-shot adversarial robustness. Specifically, we first discover that simply adding Gaussian noise greatly enhances the VLM's zero-shot performance. Then, we treat the adversarial examples with added Gaussian noise as anchors and strive to find a path in the embedding space that leads from the adversarial examples to the cleaner samples. We improve the VLMs' generalization abilities in a truly zero-shot and training-free manner compared to previous methods. Extensive experiments on 16 datasets demonstrate that our method can achieve state-of-the-art zero-shot robust performance, improving the top-1 robust accuracy by an average of $9.77\%$. The code will be publicly available.