The field of neural network security and verification is rapidly advancing, with a focus on developing innovative methods to quantify and mitigate risks associated with adversarial attacks. Recent research has explored the use of surrogate models, provable repair techniques, and proof minimization to improve the resilience and reliability of neural networks. Notably, the development of frameworks such as ProRepair and PCRLLM has shown promising results in addressing security threats and ensuring the correctness of neural network outputs. Furthermore, the application of reinforcement learning and neurosymbolic approaches has improved the efficiency and scalability of verification processes. Overall, these advances have the potential to significantly enhance the security and trustworthiness of neural networks in various applications. Noteworthy papers include: ProRepair, which proposes a novel provable neural network repair framework, and PCRLLM, which introduces a framework for proof-carrying reasoning with large language models.