CVE-2025-15381 is a vulnerability identified in the mlflow/mlflow project, specifically impacting deployments that use the 'basic-auth' application mode. Mlflow is an open-source platform widely used for managing the machine learning lifecycle, including experimentation, reproducibility, and deployment. The vulnerability arises because the tracing and assessment endpoints within the basic-auth app do not enforce permission validators. Consequently, any authenticated user, regardless of their assigned permissions (including those explicitly assigned NO_PERMISSIONS on experiments), can access sensitive trace metadata and create assessments for traces they should not be authorized to view or modify. This leads to an exposure of sensitive information (classified under CWE-200) and unauthorized data integrity modification. The vulnerability affects confidentiality by leaking trace metadata, which may include sensitive operational or experimental details, and impacts integrity by allowing unauthorized creation of assessments that could mislead or corrupt trace data analysis. The CVSS v3.0 score of 8.1 reflects the high severity, with an attack vector over the network, low attack complexity, requiring low privileges (authenticated user), no user interaction, and impacting confidentiality and integrity but not availability. The affected versions are unspecified, but any deployment running mlflow server with the basic-auth app enabled is at risk. No patches or known exploits have been reported at the time of publication.

The vulnerability poses significant risks to organizations leveraging mlflow for managing machine learning experiments and workflows. Exposure of trace metadata can lead to leakage of sensitive intellectual property, experimental results, or operational details that adversaries could use for competitive advantage or further attacks. Unauthorized creation of assessments undermines data integrity, potentially corrupting experiment evaluations or audit trails, which can mislead data scientists and decision-makers. This can degrade trust in the ML lifecycle management and impact compliance with data governance policies. Since the vulnerability requires only authenticated access, insider threats or compromised credentials can be exploited to gain unauthorized access. The lack of user interaction and network-based attack vector increases the likelihood of exploitation in environments where basic-auth is enabled without additional access controls. Organizations in sectors relying heavily on ML workflows—such as technology, finance, healthcare, and research institutions—face elevated risks of data breaches and operational disruption.

To mitigate CVE-2025-15381, organizations should first verify if their mlflow deployments use the basic-auth app. If so, immediate steps include restricting access to the mlflow server to trusted networks and users, implementing stronger authentication mechanisms beyond basic-auth (e.g., OAuth, SSO), and applying network segmentation to limit exposure. Since no patches are currently available, administrators should consider disabling the basic-auth app or replacing it with more secure authentication and authorization frameworks. Additionally, implementing strict role-based access controls (RBAC) at the application or infrastructure level can prevent unauthorized users from accessing sensitive endpoints. Monitoring and logging access to tracing and assessment endpoints should be enhanced to detect anomalous activities. Organizations should stay updated with mlflow releases for forthcoming patches addressing this vulnerability and plan prompt deployment once available. Conducting security audits and penetration tests focusing on ML infrastructure can help identify similar weaknesses.