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Not in CISA KEV

CVE-2024-37053 — mlflow

CVE-2024-37053 is a Deserialization of Untrusted Data vulnerability in mlflow. A fix is available for mlflow — see the affected versions and patch details below.

Also known asGHSA-43c4-9qgj-x742PYSEC-2026-1636
Published
Updated
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed
Exploitation data as of Nov 5, 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-2024-37053.

EPSS Exploitation Probability

via FIRST.org ↗
0.6%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs50th percentile — riskier than 50% of all scored CVEsHighest risk
0.00%0.37%0.75%1.12%0.4%0.6%May 26Sep 26Nov 26

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

Real-World Exposure

1 pkg affected
📦mlflow

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

Description

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with.

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
📦Bitnamimlflow≥ 1.1.0&&< 2.14.22.14.2

Affected Products

1 product · 1 configurations
Application
mlflowlfprojects
≥ 1.1.0
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 2.14.2 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2024-37053 is resolved across your whole dependency graph.

  3. Workarounds

    Do not deserialise data from untrusted sources: where the format allows it, restrict deserialisation to an explicit allowlist of expected types, and prefer a data-only format (JSON, Protobuf) over one that can reconstruct arbitrary objects until you can upgrade.

Frequently Asked Questions

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with.
O3 Security · Impact-Aware SCA

Is CVE-2024-37053 in your dependencies?

Find it across Bitnami, including transitive dependencies.

CVE-2024-37053: mlflow RCE — Fixed in 2.14.2