The field of AI-driven security and anomaly detection is rapidly evolving, with a focus on developing innovative solutions to address emerging threats. Recent research has explored the potential of large language models (LLMs) in generating effective malware data for detection tasks, improving face recognition accuracy through human-machine collaboration, and enhancing industrial control protocol fuzzing. Notably, the integration of LLMs with multi-agent coordination and probabilistic models has shown promise in identifying vulnerabilities and improving diagnostic accuracy. Furthermore, the development of explainable AI agents and programmatic synthesis methods has improved anomaly detection capabilities in critical IoT infrastructure and tabular data. Overall, these advancements highlight the transformative potential of AI in strengthening security and anomaly detection across various domains. Noteworthy papers include: LLM-Generated Samples for Android Malware Detection, which demonstrates the effectiveness of LLM-generated malware in enhancing scarce datasets. MALF: A Multi-Agent LLM Framework for Intelligent Fuzzing of Industrial Control Protocols, which presents a pioneering approach to identifying vulnerabilities in industrial control protocols. A Trustworthy Industrial Fault Diagnosis Architecture Integrating Probabilistic Models and Large Language Models, which improves diagnostic accuracy by over 28 percentage points compared to baseline models.