Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.5.0 or newer, enabling a maliciously uploaded… (CVE-2024-37059)
A deserialization vulnerability exists in the MLflow platform versions 0.5.0 up to but not including 2.14.2. This flaw allows a maliciously crafted PyTorch model uploaded to the platform to execute arbitrary code on the system of an end user who interacts with it.
AI Analysis
Technical Summary
CVE-2024-37059 describes a security vulnerability in MLflow where untrusted data deserialization occurs in versions starting from 0.5.0 through versions before 2.14.2. This vulnerability enables an attacker to upload a malicious PyTorch model that, when processed by the platform, can execute arbitrary code on the end user's system. This represents a critical risk as it allows remote code execution through crafted model files.
Potential Impact
Successful exploitation allows arbitrary code execution on the end user's system via malicious PyTorch models uploaded to the MLflow platform. This can lead to full system compromise depending on the privileges of the MLflow process and the environment in which it runs.
Mitigation Recommendations
A patch is available for this vulnerability. Users should upgrade MLflow to version 2.14.2 or later to remediate this issue. Since this is not a cloud service, remediation requires manual upgrade by the user or administrator.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.5.0 or newer, enabling a maliciously uploaded… (CVE-2024-37059)
Description
A deserialization vulnerability exists in the MLflow platform versions 0.5.0 up to but not including 2.14.2. This flaw allows a maliciously crafted PyTorch model uploaded to the platform to execute arbitrary code on the system of an end user who interacts with it.
Affected software
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AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
CVE-2024-37059 describes a security vulnerability in MLflow where untrusted data deserialization occurs in versions starting from 0.5.0 through versions before 2.14.2. This vulnerability enables an attacker to upload a malicious PyTorch model that, when processed by the platform, can execute arbitrary code on the end user's system. This represents a critical risk as it allows remote code execution through crafted model files.
Potential Impact
Successful exploitation allows arbitrary code execution on the end user's system via malicious PyTorch models uploaded to the MLflow platform. This can lead to full system compromise depending on the privileges of the MLflow process and the environment in which it runs.
Mitigation Recommendations
A patch is available for this vulnerability. Users should upgrade MLflow to version 2.14.2 or later to remediate this issue. Since this is not a cloud service, remediation requires manual upgrade by the user or administrator.
Technical Details
- Gcve Source
- db.gcve.eu
- Osv Id
- BIT-mlflow-2024-37059
- Osv Schema Version
- 1.5.0
- Aliases
- ["CVE-2024-37059"]
- Ecosystems
- ["Bitnami"]
- Database Specific Severity
- High
Threat ID: 6a885f2facd9273b493f830a
Added to database: 08/21/2026, 14:22:39 UTC
Last enriched: 08/21/2026, 14:41:16 UTC
Last updated: 09/10/2026, 19:36:48 UTC
Views: 19
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