Unsafe Dependency Resolution Affecting mlflow package, versions [0,]
Threat Intelligence
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-19653590
- published9 Sept 2026
- disclosed8 Sept 2026
- creditEsteban Tonglet
Introduced: 8 Sep 2026
NewCVE-2026-79721 (opens in a new tab)How to fix?
There is no fixed version for mlflow.
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 Unsafe Dependency Resolution through the model artifact loading process in the project’s model handling components. An attacker can execute code on an end user’s system by supplying a maliciously crafted model artifact that MLflow loads. When the project loads the artifact, the attacker’s payload runs in the context of the user or service loading the model, putting the affected system and any accessible data at risk.