Skip to main content
VulnerabilityPublished Updated

CVE-2025-15036: mlflow/mlflow · Path Traversal: '\..\filename

Affects mlflow/mlflowSource CVE Database V5

0
Compare

Not known to be exploited · CVSS 9.6 Critical : upgrade to 3.7.0 as a priority.

Data as of 30 Oct 2026, 11:52 UTC

CVSS 3.0 base score
9.6Critical
How severe the flaw is on the 0–10 CVSS scale.

Contested

Critical score, yet low EPSS after six months
EPSS
0.5% chance of exploitation in 30 days – higher than 48% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 3.7.0
CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 30 Mar 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 30 Oct 2026.

What to do

Upgrade mlflow to 3.7.0.

Affected software

ProductAffectedFixed in
mlflowmlflow/mlflowAffected
  • ≥ unspecified, < 3.9.0
Fixed in–
PyPImlflowpkg:pypi/mlflowOther threatsAffected
  • <3.7.0
Fixed in
  • 3.7.0

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

AI-suggested

A fix is available in mlflow version 3.7.0 and later. Users should upgrade to version 3.7.0 or newer to remediate this vulnerability. The vendor advisory from Red Hat confirms the issue and provides guidance on patching. Until upgrading, avoid processing untrusted tar.gz files with the vulnerable function to mitigate risk.

Description

A 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.

References

Community reviews

Loading reviews…