The recent developments in the field of neural network security and privacy have seen significant advancements in understanding and countering various adversarial attacks. The research community is increasingly focusing on model inversion attacks, where sensitive training data can be extracted from well-trained models, highlighting the vulnerability of neural networks. Innovations in cryptanalytic extraction methods for non-fully connected deep neural networks have also been introduced, demonstrating high fidelity in model replication. Additionally, the use of neural networks in cryptographic schemes is being explored, offering potential for creating dynamic and computationally efficient encryption methods. The field is also witnessing advancements in side-channel attacks, with practical implementations showing high accuracy in reconstructing sensitive data from physical devices like 3D printers. Furthermore, the introduction of multimodal backdoor learning toolkits and benchmarks is streamlining the evaluation of backdoor defense methods, contributing to more systematic and standardized research practices. The effectiveness of malware detection in Linux distributions is being critically examined, revealing the inadequacies of current open-source tools. Theoretical analysis and practical experiments are also shedding light on the leakage of training data from graph neural networks, prompting new research directions in data privacy. Overall, the field is moving towards more robust and comprehensive strategies for securing neural networks and protecting sensitive data, with a particular emphasis on adversarial robustness and privacy-preserving techniques.