CVE-2025-15031
Received Received - Intake
Path Traversal in MLflow pyfunc Extraction Enables Arbitrary File Write

Publication date: 2026-03-18

Last updated on: 2026-03-23

Assigner: huntr.dev

Description
A vulnerability in MLflow's pyfunc extraction process allows for arbitrary file writes due to improper handling of tar archive entries. Specifically, the use of `tarfile.extractall` without path validation enables crafted tar.gz files containing `..` or absolute paths to escape the intended extraction directory. This issue affects the latest version of MLflow and poses a high/critical risk in scenarios involving multi-tenant environments or ingestion of untrusted artifacts, as it can lead to arbitrary file overwrites and potential remote code execution.
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Meta Information
Published
2026-03-18
Last Modified
2026-03-23
Generated
2026-05-07
AI Q&A
2026-03-19
EPSS Evaluated
2026-05-05
NVD
EUVD
Affected Vendors & Products
Showing 1 associated CPE
Vendor Product Version / Range
lfprojects mlflow to 3.10.1 (inc)
Helpful Resources
Exploitability
CWE
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KEV
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CWE ID Description
CWE-22 The product uses external input to construct a pathname that is intended to identify a file or directory that is located underneath a restricted parent directory, but the product does not properly neutralize special elements within the pathname that can cause the pathname to resolve to a location that is outside of the restricted directory.
Attack-Flow Graph
AI Powered Q&A
Can you explain this vulnerability to me?

This vulnerability exists in MLflow's pyfunc extraction process where the use of the tarfile.extractall function does not properly validate file paths within tar.gz archives.

Because of this improper handling, specially crafted tar.gz files containing directory traversal sequences like '..' or absolute paths can escape the intended extraction directory.

This allows an attacker to write arbitrary files to locations outside the expected directory.


How can this vulnerability impact me? :

The vulnerability can lead to arbitrary file overwrites on the system where MLflow is running.

In multi-tenant environments or when ingesting untrusted artifacts, this can pose a high or critical risk.

Potential impacts include unauthorized modification of files and even remote code execution, which could compromise the affected system.


How does this vulnerability affect compliance with common standards and regulations (like GDPR, HIPAA)?:

I don't know


How can this vulnerability be detected on my network or system? Can you suggest some commands?

I don't know


What immediate steps should I take to mitigate this vulnerability?

I don't know


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