The field of AI security and anomaly detection is rapidly evolving, with a focus on developing innovative methods to protect against malicious attacks and identify unusual patterns in data. Recent research has explored the use of generative models, diffusion-based approaches, and explainable AI techniques to improve the accuracy and efficiency of anomaly detection systems. Additionally, there is a growing interest in developing training-free and zero-shot methods that can adapt to new datasets and scenarios without requiring extensive retraining. These advances have significant implications for real-world applications, including computer vision, medical imaging, and cybersecurity. Noteworthy papers in this area include AEDR, which proposes a novel training-free attribution method for generative models, and OCSVM-Guided Representation Learning, which introduces a custom loss formulation for unsupervised anomaly detection. Other notable works include MaXsive, which presents a high-capacity and robust training-free generative image watermarking technique, and DISTIL, which proposes a data-free trigger-inversion strategy for detecting Trojan attacks.