CVE-2026-4137 is a high-severity (CVSS 7) CWE-378 vulnerability in mlflow. A fix is available for mlflow — see the affected versions and patch details below.
Incomplete Fix for CVE-2025-10279: Insecure Temporary Directory Permissions in mlflow/mlflow
Exploitation Status
Proof-of-concept exploit code exists
- CISA’s SSVC triage found public proof-of-concept exploit code for this CVE, though no confirmed active exploitation.
- A successful exploit gives an attacker total control of the affected component, not partial access.
Exploitation and automatability from CISA’s SSVC triage for CVE-2026-4137.
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
CVE-2026-4137 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.
Where this sits among everything scored
Of 380,526 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.
Real-World Exposure
mlflowReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
Description
In mlflow/mlflow versions prior to 3.11.0, the get_or_create_nfs_tmp_dir() function in mlflow/utils/file_utils.py creates temporary directories with world-writable permissions (0o777), and the _create_model_downloading_tmp_dir() function in mlflow/pyfunc/__init__.py creates directories with group-writable permissions (0o770). These insecure permissions allow local attackers to tamper with model artifacts, such as cloudpickle-serialized Python objects, and achieve arbitrary code execution when the tampered artifacts are deserialized via cloudpickle.load(). This vulnerability is particularly critical in environments with shared NFS mounts, such as Databricks, where NFS is enabled by default. The issue is a continuation of the vulnerability class addressed in CVE-2025-10279, which was only partially fixed.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | mlflow | ≥ 1.26.0&&< 3.11.0 | 3.11.0pip install --upgrade 'mlflow==3.11.0' |
Affected Products
mlflowlfprojectsDetection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for mlflow, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update mlflow to 3.11.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2026-4137 is resolved across your whole dependency graph.
Workarounds
Do not deserialise data from untrusted sources: where the format allows it, restrict deserialisation to an explicit allowlist of expected types, and prefer a data-only format (JSON, Protobuf) over one that can reconstruct arbitrary objects until you can upgrade.
Fixing This On Your OS
If you run this on a Linux distribution, patch through your package manager against the distro's own security advisory below — it tracks the exact backported fix for your release, which can ship on a different timeline (and sometimes a different severity) than the upstream project.
Important: This flaw in MLflow allows local attackers to achieve arbitrary code execution by tampering with model artifacts in insecurely permissioned temporary directories. This is particularly critical in environments with shared Network File System (NFS) mounts, where default configurations may expose these…
Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability.Source: Red Hat security advisory for CVE-2026-4137 (CC BY 4.0)
Frequently Asked Questions
Is CVE-2026-4137 in your dependencies?
Find it across PyPI, including transitive dependencies.