Undefined Behavior in mlflow

Exploitation

No confirmed exploitation recorded. Patch Next marks exploitation only from CISA KEV.

CVSS

5.4 · CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L

EPSS

• CVE-2024-3099: 0.44% estimated exploitation activity in the next 30 days; percentile 38.45 (FIRST EPSS, 2026-11-03). This is a model estimate, not proof of exploitation or customer exposure.

As published by the source; long descriptions are shortened.

A vulnerability in mlflow/mlflow version 2.11.1 allows attackers to create multiple models with the same name by exploiting URL encoding. This flaw can lead to Denial of Service (DoS) as an authenticated user might not be able to use the intended model, as it will open a different model each time. Additionally, an attacker can exploit this vulnerability to perform data model poisoning by creating a model with the same name, potentially causing an authenticated user to become a victim by using the poisoned model. The issue stems from inadequate validation of model names, allowing for the creation of models with URL-encoded names that are treated as distinct from their URL-decoded counterparts.

Published by the source

6 Jun 2024, 21:30 UTC

Last modified by the source

7 Jul 2026, 17:56 UTC

Source last checked

26 Oct 2026, 13:21 UTC

1. Revision 2 · amended · 26 Oct 2026

   Amended from ghsa record GHSA-8f8q-q2j7-7j2m

2. Revision 1 · created · 24 Oct 2026

   Published from osv record GHSA-8f8q-q2j7-7j2m

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# Patch Next advisory brief: CVE-2024-3099

Advisory: Undefined Behavior in mlflow
IDs: CVE-2024-3099, GHSA-8f8q-q2j7-7j2m
Record: https://patchnext.com/advisories/cve-2024-3099 (revision 2, source checked 26 Oct 2026, 13:21 UTC)
Inventory: none attached. This brief comes from a public page and does not say whether any deployment is affected.
Severity: Medium (CVSS 5.4)
Exploitation: no confirmed exploitation recorded. Patch Next confirms exploitation only from CISA KEV.
Vendor response: Vendor fix available; fixed versions by package: mlflow 2.11.3. A vendor fix does not mean a deployment is fixed.

## Affected components
- PyPI mlflow: affected < 2.11.3; fixed in 2.11.3

## Suggested inspection (read-only)
- `python -m pip show mlflow`: prints the installed version. Run with the environment that serves production active.

## Supported action
Upgrade each affected component to a fixed version on its release line. For a transitive dependency, upgrade the parent package or add an override or constraint, then regenerate the lockfile. Redeploy every environment that ran an affected version.

## Required regression checks
- Run the test suite and a production build.
- Exercise the features that depend on mlflow.
- Repeat the inspection and confirm that only fixed versions remain installed.

## Rollback considerations
- Keep the previous lockfile and deployment artifact. Rolling back reinstates the vulnerable version, so apply any documented mitigation while you fix forward.

## Unresolved questions
- Which deployments run an affected version?
- Exploitation status can change. Check again before lowering the priority.
- Mark the work resolved only after the fixed version is verified in production.

## Sources
- Verified: https://osv.dev/vulnerability/GHSA-8f8q-q2j7-7j2m (OSV record GHSA-8f8q-q2j7-7j2m lists affected ranges: PyPI mlflow >=0 <2.11.3 (fixed))
- Verified: https://github.com/advisories/GHSA-8f8q-q2j7-7j2m (GitHub-reviewed advisory GHSA-8f8q-q2j7-7j2m lists affected ranges: PyPI mlflow >=0 <2.11.3)

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