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VulnerabilityPublished

CVE-2026-4137: mlflow/mlflow · Creation of Temporary File With Insecure Permissions

Affects mlflow/mlflowSource CVE Database V5

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Mentioned vulnerabilities

Not known to be exploited · CVSS 7.0 High : upgrade to 3.11.0 soon.

Data as of 29 Oct 2026, 19:04 UTC

CVSS 3.0 base score
7.0High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.1% chance of exploitation in 30 days – higher than 9% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 3.11.0
CVSS:3.0/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 18 May 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 29 Oct 2026.

What to do

Upgrade mlflow to 3.11.0.

Affected software

ProductAffectedFixed in
mlflowmlflow/mlflowAffected
  • ≥ unspecified, < 3.11.0
Fixed in–
PyPImlflowpkg:pypi/mlflowOther threatsAffected
  • <3.11.0
Fixed in
  • 3.11.0

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

If you cannot patch yet, restrict local access to shared NFS mounts and avoid running untrusted code that deserializes model artifacts. Review and harden permissions on temporary directories used by mlflow to prevent unauthorized modifications.

Description

CVE-2026-4137 is a vulnerability in mlflow/mlflow prior to version 3.11.0 where temporary directories are created with insecure permissions, allowing local attackers to tamper with model artifacts. This can lead to arbitrary code execution when deserialized via cloudpickle.load(). The issue is especially critical in shared NFS environments like Databricks. It continues a previously partially fixed vulnerability class (CVE-2025-10279).

References

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