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🐍 PyPI
Not in CISA KEV
HIGH severity

CVE-2024-27134 — mlflow

HIGHFix: mlflow/mlflow#10874

CVE-2024-27134 is a high-severity (CVSS 7) Incorrect Default Permissions vulnerability in mlflow. A fix is available for mlflow — see the affected versions and patch details below.

Excessive directory permissions in MLflow leads to local privilege escalation when using spark_udf

Also known asBIT-mlflow-2024-27134GHSA-qpgc-w4mg-6v92PYSEC-2024-224
Published
Updated
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed
Exploitation data as of Nov 6, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

No confirmed exploitation observed yet

  • A successful exploit gives an attacker total control of the affected component, not partial access.
  • CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.

Exploitation and automatability from CISA’s SSVC triage for CVE-2024-27134.

EPSS Exploitation Probability

via FIRST.org ↗
0.1%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs2th percentile — riskier than 2% of all scored CVEsHighest risk
0.00%0.21%0.41%0.62%0.0%0.1%May 26Sep 26Nov 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

Where this sits among everything scored

Real-World Exposure

1 pkg affected
🐍mlflow

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Excessive directory permissions in MLflow leads to local privilege escalation when using spark_udf. This behavior can be exploited by a local attacker to gain elevated permissions by using a ToCToU attack. The issue is only relevant when the spark_udf() MLflow API is called.

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPImlflowall versions2.16.0pip install --upgrade 'mlflow==2.16.0'

Affected Products

1 product · 1 configurations
Application
mlflowlfprojects
< 2.16.0
range

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for mlflow, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update mlflow to 2.16.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2024-27134 is resolved across your whole dependency graph.

  3. Workarounds

    Close the privilege gap rather than the entry point: audit which accounts, roles and service identities can reach the affected operation, drop the component to the least privilege it actually needs, and review file and directory permissions created by earlier installs — a default left in place is what makes this reachable.

Frequently Asked Questions

Excessive directory permissions in MLflow leads to local privilege escalation when using spark_udf. This behavior can be exploited by a local attacker to gain elevated permissions by using a ToCToU attack. The issue is only relevant when the spark_udf() MLflow API is called.
O3 Security · Impact-Aware SCA

Is CVE-2024-27134 in your dependencies?

Find it across PyPI, including transitive dependencies.

CVE-2024-27134: mlflow PrivEsc — Fixed in 2.16.0