Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded… (CVE-2024-37054)
A deserialization vulnerability exists in the MLflow platform versions 0.9.0 and newer up to but not including 2.14.2. This flaw allows a maliciously crafted PyFunc model to execute arbitrary code on a user's system when the model is interacted with. The vulnerability is classified as high severity. A patch is available to address this issue.
AI Analysis
Technical Summary
CVE-2024-37054 describes a deserialization of untrusted data vulnerability in MLflow versions >=0.9.0 and <2.14.2. An attacker can upload a malicious PyFunc model that, when loaded or interacted with, can execute arbitrary code on the end user's system. This vulnerability arises from unsafe deserialization practices within the MLflow platform. No CVSS score is provided, but the impact is considered high due to the potential for arbitrary code execution.
Potential Impact
Successful exploitation allows arbitrary code execution on the end user's system via a malicious PyFunc model. This can lead to full compromise of the affected system depending on the privileges of the MLflow process. There are no known exploits in the wild at this time.
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 PyFunc models to mitigate risk.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded… (CVE-2024-37054)
Description
A deserialization vulnerability exists in the MLflow platform versions 0.9.0 and newer up to but not including 2.14.2. This flaw allows a maliciously crafted PyFunc model to execute arbitrary code on a user's system when the model is interacted with. The vulnerability is classified 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
CVE-2024-37054 describes a deserialization of untrusted data vulnerability in MLflow versions >=0.9.0 and <2.14.2. An attacker can upload a malicious PyFunc model that, when loaded or interacted with, can execute arbitrary code on the end user's system. This vulnerability arises from unsafe deserialization practices within the MLflow platform. No CVSS score is provided, but the impact is considered high due to the potential for arbitrary code execution.
Potential Impact
Successful exploitation allows arbitrary code execution on the end user's system via a malicious PyFunc model. This can lead to full compromise of the affected system depending on the privileges of the MLflow process. There are no known exploits in the wild at this time.
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 PyFunc models to mitigate risk.
Technical Details
- Gcve Source
- db.gcve.eu
- Osv Id
- BIT-mlflow-2024-37054
- Osv Schema Version
- 1.5.0
- Aliases
- ["CVE-2024-37054"]
- Ecosystems
- ["Bitnami"]
- Database Specific Severity
- High
Threat ID: 6a885f2facd9273b493f8314
Added to database: 08/21/2026, 14:22:39 UTC
Last enriched: 08/21/2026, 14:42:01 UTC
Last updated: 09/10/2026, 19:36:48 UTC
Views: 28
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