Undefined Behavior in mlflowGHSA-8f8q-q2j7-7j2m
MEDIUMGHSA-8f8q-q2j7-7j2m is a medium-severity (CVSS 5.4) CWE-475 vulnerability in mlflow. 1 public exploit or proof-of-concept reference is indexed, so weaponization risk is real. A fix is available for mlflow — see the affected versions and patch details below.
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.
Exploitation and automatability from CISA’s SSVC triage for GHSA-8f8q-q2j7-7j2m.
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
Where this sits among everything scored
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
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.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | mlflow | all versions | 2.11.3pip install --upgrade 'mlflow==2.11.3' |
Affected Products
mlflowlfprojectsResearch use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.
Detection & 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 2.11.3 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-8f8q-q2j7-7j2m is resolved across your whole dependency graph.
Workarounds
Cap what an attacker can consume: apply request size, rate and timeout limits in front of the affected component, and run it with memory and CPU limits so exhaustion degrades one worker rather than the whole service.
Frequently Asked Questions
Is GHSA-8f8q-q2j7-7j2m in your dependencies?
Find it across PyPI, including transitive dependencies.