CVE-2023-3765
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 · uneditedAbsolute Path Traversal in GitHub repository mlflow/mlflow prior to 2.5.0.
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 · moderate confidenceAn absolute path traversal vulnerability exists in MLflow versions prior to 2.5.0, allowing attackers to access files outside the intended directory by supplying absolute file paths. This enables unauthorized file system access including reading sensitive files, configuration data, or code from arbitrary locations on the server.
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< 2.5.0CVSS 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 MLflow versionRun 'pip show mlflow' or check the installed package version via your package manager. Alternatively, query the MLflow server's API endpoint if running.Affected if The installed version is prior to 2.5.0 (e.g., 2.4.x, 2.3.x, etc.)
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Confirm MLflow server is exposedDetermine if the MLflow tracking server or artifact server is accessible over HTTP/HTTPS to external users or untrusted inputs. Check network configuration and firewall rules.Affected if The MLflow server is network-accessible to untrusted users or processes without proper authentication or network segmentation.
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Check artifact storage backend configurationReview MLflow configuration for 'artifact_location' or 'default-artifact-root' settings. Identify what storage backend is used (local filesystem, S3, GCS, Azure Blob, etc.).Affected if Artifact storage uses a local filesystem backend and paths are not restricted to a specific directory tree.
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Verify path handling in MLflow API endpointsTest whether MLflow API endpoints that handle file paths accept absolute path inputs. Look for endpoints related to artifact upload/download or model serving.Affected if The API accepts absolute file paths in request parameters and returns file contents from outside the intended artifact directory.
You are affected if MLflow version is below 2.5.0 AND the server is accessible to process untrusted requests that can supply absolute file paths to MLflow artifact or model APIs.
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 · scoped2.5.0
Upgrade MLflow to version 2.5.0 or later. Implement strict path validation to ensure user-supplied paths are normalized and confined within allowed directories.
2.5.0
- Identify the current installed version of MLflow using 'pip show mlflow' or 'mlflow --version'
- Upgrade MLflow to version 2.5.0 or later using 'pip install --upgrade mlflow>=2.5.0'
- Verify the upgrade was successful by checking the version with 'pip show mlflow'
- Restart any MLflow tracking servers or related services that are running to ensure the patched version is loaded
- If using a package manager like conda, update accordingly: 'conda install -c conda-forge mlflow>=2.5.0' or 'pip install --upgrade mlflow>=2.5.0' within the conda environment
Generated from the published advisory — verify against the referenced sources before acting.
- Consultation4.0 h
- Implementation8.0 h
- Testing6.0 h
- Review / QA4.0 h
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Scan for this in your stack
Free · runs locallyCheck whether your project pulls in CVE-2023-3765 — 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-2023-3765 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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- Verified mitigations, workarounds, and config changes
- Version or environment caveats, and links to real fixes
- No weaponised exploit code, or anything meant to cause harm
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