The field of WiFi-based human sensing and security is rapidly advancing, with a focus on developing innovative solutions for pose estimation, person identification, and security threats. Recent research has explored the use of deep learning techniques to improve the accuracy and efficiency of these systems. WiFi-based human pose estimation has shown promising results, with the development of novel frameworks that can accurately estimate human pose using WiFi channel state information. Furthermore, person identification via WiFi signals has emerged as a viable alternative to traditional vision-based systems, with transformer-based methods achieving high classification accuracy. In the realm of security, machine learning-based approaches have been proposed to detect DNS tunneling and jamming attacks, demonstrating improved detection rates and robustness compared to traditional methods. Notably, the use of hybrid feature selection and auto-classification modules has enhanced the effectiveness of these systems. Overall, the field is moving towards the development of more accurate, efficient, and reliable WiFi-based human sensing and security solutions. Noteworthy papers include: VST-Pose, which introduces a novel deep learning framework for accurate and continuous pose estimation using WiFi channel state information. JamShield, which presents a dynamic jamming detection system trained on a collected over-the-air and publicly available dataset, utilizing hybrid feature selection and an auto-classification module.