The field of AI security and deep research is rapidly evolving, with a focus on developing more robust and trustworthy models. Recent developments have highlighted the importance of evaluating the safety and security of large language models, particularly in the context of web-augmented models. Researchers are working to create more comprehensive frameworks for evaluating and improving the security of these models, including the development of benchmarks and datasets specifically designed for this purpose. Notable papers in this area include CREST-Search, which presents a framework for systematically exposing risks in web-augmented language models, and DeepResearchGuard, which introduces a comprehensive framework for evaluating the safety of deep research frameworks. Other notable papers include PACEbench, which provides a practical AI cyber-exploitation benchmark, and CTIArena, which evaluates LLM performance on heterogeneous cyber threat intelligence. Additionally, researchers are exploring the development of smaller, domain-specific language models, such as CyberPal 2.0, which has shown promising results in cybersecurity tasks. Overall, the field is moving towards more specialized and robust models, with a focus on ensuring the safety and security of AI systems.