The field of red-teaming and security analysis is rapidly evolving, with a focus on developing innovative methods to identify and exploit vulnerabilities in various systems, including code agents, multimodal models, and research management applications. Recent developments have led to the creation of adaptive red-teaming agents, such as RedCodeAgent and ARMs, which can systematically uncover vulnerabilities in diverse code agents and multimodal models. These agents leverage existing knowledge, dynamically select effective red-teaming tools, and identify vulnerabilities that might otherwise be overlooked. Furthermore, research has also focused on developing new attack strategies, such as plug-and-play attacks, and improving the robustness of large language models. Noteworthy papers in this area include RedCodeAgent, which achieves higher attack success rates and lower rejection rates with high efficiency, and ARMs, which proposes 11 novel multimodal attack strategies and integrates 17 red-teaming algorithms. Additionally, papers like LegalSim and PentestMCP have also made significant contributions to the field, with LegalSim exploring how AI systems can exploit procedural weaknesses in codified rules and PentestMCP supporting common penetration testing tasks. Overall, these advancements highlight the importance of continued research in red-teaming and security analysis to stay ahead of emerging threats and vulnerabilities.