The field of vision-language models is rapidly advancing, with a growing focus on improving adversarial robustness. Recent research has highlighted the vulnerability of these models to adversarial attacks, which can compromise their performance and reliability. To address this challenge, researchers are exploring new methods to enhance the robustness of vision-language models, including the development of novel attack frameworks and defense strategies. Notably, some papers have proposed innovative approaches to improve the robustness of vision-language models, such as the use of adversarial mixture prompt tuning and zero-shot vision encoder grafting. The paper 'Enhancing Adversarial Robustness of Vision Language Models via Adversarial Mixture Prompt Tuning' is particularly noteworthy, as it presents a novel method to enhance the generalization of vision-language models towards various adversarial attacks. Another notable paper is 'Zero-Shot Vision Encoder Grafting via LLM Surrogates', which proposes a promising strategy to reduce the costs of training vision-language models by leveraging small surrogate models.