PyPI: mlflow
CVE-2026-10803
Safety vulnerability ID: SFTY-20260604-43273
Affected versions of the MLflow package are vulnerable to Insecure Randomness due to the use of weak hash functions in dataset digest computation. The `mlflow.data.digest_utils` function in `mlflow/data/digest_utils.py` employs deterministic sampling, which can lead to predictable hash collisions. An attacker can exploit this vulnerability by crafting specific datasets that result in hash collisions, potentially leading to data integrity issues or unauthorized data manipulation on the local host.
Overview
MLflow: Deterministic sampling in dataset digest enables predictable collisions
Advisory
mlflow – Use of a Broken or Risky Cryptographic Algorithm
How to Fix
Mitigation and Workarounds
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Vulnerable Functions
Functions linked to known vulnerabilities.
References
- https://getsafety.com/vulnerabilities/SFTY-20260604-43273/CVE-2026-10803
- https://nvd.nist.gov/vuln/detail/CVE-2026-10803
- https://github.com/mlflow/mlflow/issues/22419
- https://github.com/mlflow/mlflow/pull/22420
- https://github.com/mlflow/mlflow
- https://vuldb.com/cve/CVE-2026-10803
- https://vuldb.com/submit/831462
- https://vuldb.com/vuln/368252
- https://vuldb.com/vuln/368252/cti
- https://github.com/pypa/advisory-database/tree/main/vulns/mlflow/PYSEC-2026-195.yaml
- https://github.com/advisories/GHSA-5qmp-p3c4-72qj
Verified by Safety
Our Cybersecurity Intelligence Team reviewed this vulnerability. We combine public data with our own research to find issues not yet reported to public sources.
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