HighVulnerability

CVE-2024-37053: MLflow deserialization of untrusted data via scikit-learn model

Source: NVD(opens in a new tab)
Identifier: CVE-2024-37053
Published: June 4, 2024
Record updated: October 9, 2026

Affected: mlflow >= 1.1.0, <= 2.14.1
Fixed in: No fixed version was stated when the source was last read.
Known exploitation: Not listed in the CISA Known Exploited Vulnerabilities catalog at the last check.
EPSS: 0.6%

Summary

CVE-2024-37053 is a deserialization of untrusted data flaw (CWE-502) in the MLflow platform, versions 1.1.0 and newer. A maliciously uploaded scikit-learn model can run arbitrary code on an end user's system when that user interacts with it. NVD has not yet provided an assessment, and HiddenLayer is listed as the source.

Mitigation

The source does not state a fix yet. Check the original advisory for updates.

Details

Scores and exploitation

CVSS score: 8.8(high)
EPSS: 0.6%Estimated probability that this CVE is exploited in the next 30 days, from the FIRST Exploit Prediction Scoring System. Read on November 4, 2026.

Weaknesses and techniques

Weakness (CWE): CWE-502
CWE-502
Attack pattern (CAPEC): CAPEC-586
AI threat technique (MITRE ATLAS): AML.T0010

Affected

Packages: mlflow@>= 1.1.0, <= 2.14.1
Vendors: HuggingFace

Classification

Attack type: Supply chain
Attack sophistication: Moderate
AI component targeted: Framework
Impact: Integrity,
Confidentiality
Labels: security

How this record was produced

Summaries, mitigations and labels on this page are generated automatically from the source. The linked source is authoritative. The source gives this advisory a description and no title, so the title above is a headline generated from the source text.

Original source: https://nvd.nist.gov/vuln/detail/CVE-2024-37053

First tracked: February 15, 2026 at 08:42 PM ET, from NVD/CVE Database

Classifier: claude-haiku-5-5, prompt v4

Classifier confidence: 90% (self-assessed)

Affected packages in the Exposure Registry

Matched by package name and ecosystem. Each entry shows whether the package delegates to a language model and how many tracked packages depend on it.

mlflowPyPILLM dependency since 2024-05-30 · 17 tracked dependents

Summary

Mitigation

Affected packages in the Exposure Registry

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