Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded… (CVE-2024-37053)
A deserialization vulnerability exists in MLflow platform versions 1.1.0 and newer up to but not including 2.14.2. This flaw allows a maliciously crafted scikit-learn model uploaded to the platform to execute arbitrary code on the system when the model is interacted with. The vulnerability is rated as high severity. A patch is available to address this issue.
AI Analysis
Technical Summary
MLflow versions >=1.1.0 and <2.14.2 are vulnerable to deserialization of untrusted data, which can be exploited by uploading a malicious scikit-learn model. When the model is loaded or interacted with, arbitrary code execution can occur on the end user's system. This vulnerability is identified as CVE-2024-37053 and has been assigned a high severity rating. There is no CVSS score provided. The vulnerability affects the MLflow platform and is not related to a cloud service. A patch is available to remediate this issue.
Potential Impact
Successful exploitation allows an attacker to execute arbitrary code on the system of an end user interacting with a maliciously uploaded scikit-learn model in MLflow. This can lead to full system compromise depending on the privileges of the MLflow process.
Mitigation Recommendations
A patch is available for this vulnerability. Users should upgrade MLflow to version 2.14.2 or later to remediate this issue. Until patched, avoid interacting with untrusted or unverified scikit-learn models within MLflow.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded… (CVE-2024-37053)
Description
A deserialization vulnerability exists in MLflow platform versions 1.1.0 and newer up to but not including 2.14.2. This flaw allows a maliciously crafted scikit-learn model uploaded to the platform to execute arbitrary code on the system when the model is interacted with. The vulnerability is rated as high severity. A patch is available to address this issue.
Affected software
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AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
MLflow versions >=1.1.0 and <2.14.2 are vulnerable to deserialization of untrusted data, which can be exploited by uploading a malicious scikit-learn model. When the model is loaded or interacted with, arbitrary code execution can occur on the end user's system. This vulnerability is identified as CVE-2024-37053 and has been assigned a high severity rating. There is no CVSS score provided. The vulnerability affects the MLflow platform and is not related to a cloud service. A patch is available to remediate this issue.
Potential Impact
Successful exploitation allows an attacker to execute arbitrary code on the system of an end user interacting with a maliciously uploaded scikit-learn model in MLflow. This can lead to full system compromise depending on the privileges of the MLflow process.
Mitigation Recommendations
A patch is available for this vulnerability. Users should upgrade MLflow to version 2.14.2 or later to remediate this issue. Until patched, avoid interacting with untrusted or unverified scikit-learn models within MLflow.
Technical Details
- Gcve Source
- db.gcve.eu
- Osv Id
- BIT-mlflow-2024-37053
- Osv Schema Version
- 1.5.0
- Aliases
- ["CVE-2024-37053"]
- Ecosystems
- ["Bitnami"]
- Database Specific Severity
- High
Threat ID: 6a885f2facd9273b493f8316
Added to database: 08/21/2026, 14:22:39 UTC
Last enriched: 08/21/2026, 14:42:09 UTC
Last updated: 09/10/2026, 19:36:48 UTC
Views: 30
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