CVE-2025-15036
Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.
NVD · uneditedA path traversal vulnerability exists in the `extract_archive_to_dir` function within the `mlflow/pyfunc/dbconnect_artifact_cache.py` file of the mlflow/mlflow repository. This vulnerability, present in versions before v3.7.0, arises due to the lack of validation of tar member paths during extraction. An attacker with control over the tar.gz file can exploit this issue to overwrite arbitrary files or gain elevated privileges, potentially escaping the sandbox directory in multi-tenant or shared cluster environments.
Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.
dbcve analysis · high confidenceA path traversal vulnerability in mlflow's `extract_archive_to_dir` function allows attackers to craft malicious tar.gz archives containing files with relative paths (e.g., ../../../etc/passwd). Without validating extracted member paths, the extraction process writes files outside the intended sandbox directory, enabling arbitrary file overwrite and privilege escalation.
Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.
Affected products & versions What the vendor confirmedThe version ranges the vendor confirmed as vulnerable. If your version sits inside a range here, treat yourself as exposed until you have upgraded.
NVD · CPE data< 3.9.0CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.
From the vector- Attack vector
- Network
- Complexity
- Low
- Privileges
- None
- User interaction
- None
- Scope
- Changed
- Confidentiality
- High
- Integrity
- High
- Availability
- High
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H
Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.
dbcve checksWork through these to decide whether this CVE applies to you.
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Identify installed mlflow versionRun 'pip show mlflow' or 'python -c "import mlflow; print(mlflow.__version__)"' to get the installed version numberAffected if The installed version is lower than 3.9.0
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Locate the vulnerable moduleCheck if the file pyfunc/dbconnect_artifact_cache.py exists in the mlflow installation directory. Use 'python -c "import mlflow.pyfunc.dbconnect_artifact_cache; print(mlflow.pyfunc.dbconnect_artifact_cache.__file__)"' to find its pathAffected if The file exists and the version is below 3.9.0
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Inspect for path validation in extraction codeReview the extract_archive_to_dir function source code for presence of path traversal checks. Look for validation that rejects entries starting with '/' or containing '..' sequences before extraction proceedsAffected if No such validation is found - the code directly extracts archive members without checking for absolute paths or parent directory references
A user is affected if they have mlflow installed with version below 3.9.0 and the extract_archive_to_dir function performs extraction without validating tar member paths.
Generated from the published advisory. Verify against your own configuration.
Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.
dbcve · scoped3.9.0
Upgrade to mlflow v3.7.0 or later, which implements proper validation of tar member paths before extraction. If immediate upgrade is not possible, implement path validation to ensure extracted paths remain within the target directory.
3.9.0
- 1. Identify the current mlflow version by running `pip show mlflow` or checking your dependency files
- 2. Upgrade mlflow to version 3.9.0 or later using `pip install --upgrade mlflow>=3.9.0` or update your dependency file
- 3. Verify the upgrade was successful by running `pip show mlflow` and confirming the version number
- 4. Test that your mlflow deployment still functions correctly after the upgrade
Generated from the published advisory — verify against the referenced sources before acting.
- Consultation2.0 h
- Implementation4.0 h
- Testing3.0 h
- Review / QA2.0 h
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Free · runs locallyCheck whether your project pulls in CVE-2025-15036 — or any other known-vulnerable package — straight from your lock files. Free and open source; it runs locally and uploads nothing.
References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.
Primary sourcesPractitioner notes
ContributedPeer-ranked notes from engineers who’ve handled CVE-2025-15036 in production — separate from our analysis above.
The advisory tells you what broke. It rarely tells you what actually worked. If you’ve dealt with this one, that detail is what the next engineer is searching for.
- The version that genuinely resolved it — not the one the vendor claimed
- A config change or rule that shut the vector down
- A gotcha in the upgrade path that cost you an afternoon
No notes yet
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- Version or environment caveats, and links to real fixes
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