ML operations, data scientists, and developers are confronted with significant security challenges, particularly concerning false positives and false negatives in ML model scanning. High rates of false positives clutter systems with unnecessary alerts, while false negatives allow dangerous models to go undetected, eroding trust in security measures. Addressing these issues, JFrog has integrated a model scanning solution with Hugging Face, significantly reducing false positives by 96% and identifying threats missed by traditional scanners. This integration enhances AI/ML security by extending scrutiny to model files and configuration files, offering detailed explanations of potential threats. JFrog's methodology involves advanced evidence extraction to differentiate genuine threats from false alarms, improving both accuracy and transparency in model security. This innovation is designed to protect AI applications by identifying threats during both the loading and prediction stages of model workflows. By providing evidence-backed security assessments and a secure proxy to block high-risk models, JFrog’s approach aims to enhance model security without overwhelming users with alerts, fostering a more reliable and secure AI development environment.