GHSA-8f8q-q2j7-7j2m — mlflow
MEDIUMGHSA-8f8q-q2j7-7j2m is a medium-severity (CVSS 5.4) CWE-475 vulnerability in mlflow. 1 public exploit reference exists, so weaponization risk is real. A fix is available for mlflow — see the affected versions and patch details below.
Undefined Behavior in mlflow
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
EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.
How urgent is this, really
GHSA-8f8q-q2j7-7j2m plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.
Where this sits among everything scored
Of 379,145 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.
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' |
Research 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
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like GHSA-8f8q-q2j7-7j2m can be triaged on real exposure rather than presence alone.
Tailored to GHSA-8f8q-q2j7-7j2m. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-8f8q-q2j7-7j2m in your dependencies?
O3 Security finds GHSA-8f8q-q2j7-7j2m across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.