Recent research in security is increasingly focused on addressing vulnerabilities in machine learning models, particularly those used in software security and large language models (LLMs). Investigations into data leakage have revealed that common training practices can inflate the perceived effectiveness of secret detection models, highlighting the need for more robust evaluation methods. Meanwhile, as traditional CAPTCHAs fail against advanced GUI agents, new frameworks are being developed to exploit the cognitive gap between humans and machines, ensuring more effective defenses. The rise of federated learning has introduced unique security challenges, such as backdoor attacks that exploit specific neural network layers, necessitating layer-aware detection strategies. Additionally, the proliferation of prompt injection attacks against LLMs has prompted systematic reviews to categorize and enhance mitigation strategies. Collectively, these efforts indicate a shift toward more nuanced and proactive security measures, essential for safeguarding applications in an era of rapidly evolving AI capabilities.