The field of Software-Defined Networking (SDN) is moving towards a greater emphasis on security and vulnerability detection. Researchers are exploring new methods to identify and exploit potential vulnerabilities in distributed SDN controllers and SD-WAN architectures. One notable trend is the use of fuzzing and machine learning-based approaches to discover and classify vulnerabilities. Another area of focus is the development of tools and methodologies to improve the security of Internet protocols, such as DNS. Noteworthy papers in this area include: Ambusher, which introduces a novel protocol state fuzzing methodology to discover vulnerabilities in distributed SDN controllers. Heimdallr, which proposes a deep learning-based approach to fingerprint SD-WAN control-plane architecture via encrypted control traffic. LAPRAD, which leverages large language models to assist in protocol attack discovery. Trust, But Verify, which evaluates the reliability of AI-generated code for SDN controllers.