The research presented by JFrog Security Research at Black Hat USA 2024 highlights vulnerabilities in open-source machine learning operations (MLOps) platforms, identifying more than 20 CVEs and revealing how real-world attacks can exploit these systems. The study explores core MLOps features, inherent versus implementation vulnerabilities, and best practices for deploying these platforms securely. Inherent vulnerabilities, such as malicious models and datasets that can execute arbitrary code, pose significant risks, while implementation issues like lack of authentication and container escapes exacerbate the threat landscape. The research emphasizes the need for robust security measures, including authentication, container isolation, and awareness of unsafe model formats, to mitigate the potential for attacks. Additionally, JFrog provides solutions like the XSSGuard extension for JupyterLab to defend against specific vulnerabilities and promotes the use of the JFrog Platform for securing ML models through controls such as RBAC, versioning, and security scanning, ensuring the integrity of AI/ML releases.