The field of wireless security and localization is rapidly evolving, with a focus on improving the accuracy and robustness of localization systems and enhancing the security of wireless communications. Recent research has explored the use of machine learning and deep learning techniques to improve localization accuracy and robustness, as well as the development of new security protocols and architectures to protect against emerging threats. Notably, the use of Wi-Fi Channel State Information (CSI) as a biometric modality has been proposed, but its security properties and vulnerabilities have also been highlighted. Furthermore, the development of new localization systems, such as those using LoRa and phase-time arrays, has shown promising results. In the area of software supply chain security, researchers have proposed new methods for analyzing and mitigating threats, including the use of logical attack graphs and meta-learning. Additionally, the development of secure and user-friendly architectures for mobile Web3 applications has been explored. Some noteworthy papers in this area include: SoK: Security Evaluation of Wi-Fi CSI Biometrics, which provides a comprehensive analysis of the security properties of Wi-Fi CSI biometrics. SPOT: Single-Shot Positioning via Trainable Near-Field Rainbow Beamforming, which proposes a novel localization system using phase-time arrays. LoRaCompass: Robust Reinforcement Learning to Efficiently Search for a LoRa Tag, which develops a robust reinforcement learning model for localizing LoRa tags. Meta-SimGNN: Adaptive and Robust WiFi Localization Across Dynamic Configurations and Diverse Scenarios, which proposes a novel WiFi localization system using graph neural networks and meta-learning. SecureSign: Bridging Security and UX in Mobile Web3 through Emulated EIP-6963 Sandboxing, which presents a secure and user-friendly architecture for mobile Web3 applications.