The field of vision-language model security and adversarial defense is rapidly advancing, with a focus on detecting and mitigating backdoor attacks and improving model robustness. Researchers are exploring new detection methods that operate without prior knowledge of training datasets or backdoor triggers, and are achieving high detection accuracy and efficiency. Additionally, there is a growing interest in concept-level vulnerabilities and semantic concept-level attacks, which pose a significant threat to vision-language models. Noteworthy papers include Assimilation Matters, which introduces a novel model-level detection framework that leverages feature assimilation properties to detect backdoors, and Concept-Guided Backdoor Attack, which proposes a new paradigm for backdoor attacks that operates at the semantic concept level. FeatureLens is also a notable work, which provides a lightweight and interpretable framework for detecting adversarial examples based on image features. Overall, the field is moving towards more innovative and effective solutions for ensuring the security and reliability of vision-language models.