The recent developments in the field of cybersecurity have seen significant advancements in the detection and defense against sophisticated threats. Researchers are increasingly leveraging large language models (LLMs) and deep learning techniques to enhance the efficiency and accuracy of vulnerability detection and backdoor defense mechanisms. The integration of LLMs, such as those fine-tuned for Domain Generation Algorithm (DGA) and DNS exfiltration detection, has shown remarkable performance in real-time detection tasks, outperforming traditional methods. Additionally, the use of deep learning models, particularly CodeBERT, for automated vulnerability detection in software has demonstrated superior precision and recall compared to conventional static application security testing (SAST) tools. These innovations not only improve the detection of known vulnerabilities but also show promise in identifying previously unknown threats. The field is moving towards more unified and comprehensive defense frameworks, such as the two-step defense system that exposes and then defends against backdoor attacks in neural networks. This approach, which includes novel techniques like Clean Unlearning, is proving to be a robust method for enhancing model security. Overall, the trend is towards more sophisticated, AI-driven solutions that can adapt to the evolving landscape of cyber threats.