The recent advancements in the research area demonstrate a strong focus on enhancing the robustness and privacy of machine learning models against various adversarial threats. A significant trend is the development of novel techniques that balance privacy protection with model utility, addressing the dual challenges of adversarial robustness and data leakage. Key innovations include methods that leverage data poisoning to defend against model inversion attacks, frameworks that optimize latent space for adversarial purification, and systems that detect insider threats with high precision by considering contextual anomalies. Additionally, there is a notable shift towards domain-agnostic approaches for black-box model attribute reverse engineering and robust query-driven cardinality estimation under out-of-distribution conditions. These developments collectively push the boundaries of model security, emphasizing the need for adaptive and retraining-free defense mechanisms. Notably, the work on adversarial purification via latent space optimization and the high-precision insider threat detection system stand out for their innovative approaches and practical applicability.