The field of vision-language models is rapidly evolving, with a growing focus on security and robustness. Recent research has highlighted the vulnerability of these models to backdoor attacks, data poisoning, and other malicious threats. To address these challenges, researchers are developing innovative defense strategies, including regularization techniques, adversarial training, and safety alignment methods. These approaches aim to improve the robustness of vision-language models against various types of attacks, while preserving their performance on benign inputs. Notably, some papers have introduced new benchmarks and evaluation frameworks to assess the security of vision-language models, providing a more comprehensive understanding of their vulnerabilities. Overall, the field is moving towards developing more secure and reliable vision-language models that can be deployed in real-world applications. Noteworthy papers include: Robust Anti-Backdoor Instruction Tuning in LVLMs, which introduces a lightweight defense framework to prevent backdoor attacks in large visual language models. TED-LaST, which proposes a novel defense strategy against adaptive backdoor attacks in deep neural networks. ALKALI, which exposes a critical geometric blind spot in alignment and introduces a comprehensive adversarial benchmark for evaluating the security of large language models.