Exposure of Sensitive System Information to an Unauthorized Control Sphere Affecting mlflow package, versions [,3.11.0rc1)
Threat Intelligence
This vulnerability is trending on Twitter; this may indicate a growing threat.
Snyk has a proof-of-concept or detailed explanation of how to exploit this vulnerability.
The probability is the direct output of the EPSS model, and conveys an overall sense of the threat of exploitation in the wild. The percentile measures the EPSS probability relative to all known EPSS scores. Note: This data is updated daily, relying on the latest available EPSS model version. Check out the EPSS documentation for more details.
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Test your applications- Snyk IDSNYK-PYTHON-MLFLOW-15870196
- published1 Apr 2026
- disclosed27 Mar 2026
- creditraltheo
Introduced: 27 Mar 2026
CVE-2025-15381 (opens in a new tab)How to fix?
Upgrade mlflow to version 3.11.0rc1 or higher.
Overview
mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.
Affected versions of this package are vulnerable to Exposure of Sensitive System Information to an Unauthorized Control Sphere in the tracing and assessment endpoints. An attacker can access sensitive trace metadata and create unauthorized assessments by authenticating with any user account, even those with no permissions on the experiment.