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VulnerabilityPublished Updated

CVE-2024-0520: mlflow/mlflow · Path Traversal

Affects mlflow/mlflowSource CVE Database V5

0

Not known to be exploited · CVSS 10.0 Critical : upgrade to 2.9.0 as a priority.

Data as of 26 Oct 2026, 12:54 UTC

CVSS 3.0 base score
10.0Critical
How severe the flaw is on the 0–10 CVSS scale.
EPSS
2.4% chance of exploitation in 30 days – higher than 83% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 2.9.0
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 6 Jun 2024

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: 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 26 Oct 2026.

What to do

Upgrade mlflow to 2.9.0.

Affected software

ProductAffectedFixed in
PyPImlflowpkg:pypi/mlflowOther threatsAffected
  • <=8.2.1
  • <2.9.0
Fixed in
  • 2.9.0

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

AI-suggested

European organizations should immediately upgrade mlflow to version 2.9.0 or later, where the vulnerability is fixed. Until patching is possible, implement strict network-level controls to restrict access to mlflow services, especially from untrusted networks. Employ Web Application Firewalls (WAFs) or Intrusion Prevention Systems (IPS) to detect and block suspicious HTTP requests containing path traversal patterns. Conduct thorough input validation and sanitization on any user-supplied filenames or URLs before processing. Monitor logs for unusual file write activities or command execution attempts related to mlflow processes. Isolate mlflow instances in segmented network zones with minimal privileges to limit potential damage. Regularly audit and review machine learning pipelines and data sources for anomalies. Educate development and operations teams about secure coding practices related to file handling and external data ingestion. Finally, maintain an incident response plan tailored to AI infrastructure compromises.

Description

A vulnerability in mlflow/mlflow version 8.2.1 allows for remote code execution due to improper neutralization of special elements used in an OS command ('Command Injection') within the `mlflow.data.http_dataset_source.py` module. Specifically, when loading a dataset from a source URL with an HTTP scheme, the filename extracted from the `Content-Disposition` header or the URL path is used to generate the final file path without proper sanitization. This flaw enables an attacker to control the file path fully by utilizing path traversal or absolute path techniques, such as '../../tmp/poc.txt' or '/tmp/poc.txt', leading to arbitrary file write. Exploiting this vulnerability could allow a malicious user to execute commands on the vulnerable machine, potentially gaining access to data and model information. The issue is fixed in version 2.9.0.

References

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