CVE-2026-2393 is a high-severity (CVSS 7.1) Server-Side Request Forgery (SSRF) vulnerability in mlflow. A fix is available for mlflow — see the affected versions and patch details below.
Server-Side Request Forgery (SSRF) in mlflow/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 CVE-2026-2393.
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 Server-Side Request Forgery (SSRF) vulnerability exists in MLflow versions prior to 3.9.0. The _create_webhook() function in mlflow/server/handlers.py accepts a user-controlled url parameter without validation, and the _send_webhook_request() function in mlflow/webhooks/delivery.py sends HTTP POST requests to this attacker-controlled URL. This allows an authenticated attacker to force the MLflow backend to send HTTP requests to internal services, cloud metadata endpoints, or arbitrary external servers. The lack of input sanitization, URL scheme filtering, or allowlist validation on the webhook URL enables exploitation, potentially leading to cloud credential theft, internal network access, and data exfiltration.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | mlflow | all versions | 3.9.0pip install --upgrade 'mlflow==3.9.0' |
Affected Products
mlflowlfprojectsDetection & 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 3.9.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2026-2393 is resolved across your whole dependency graph.
Workarounds
Restrict outbound requests from the affected component to an allowlist of hosts, block access to link-local and internal address ranges at the network layer, and require authentication on internal services so a forged request cannot reach them unauthenticated.
Fixing This On Your OS
If you run this on a Linux distribution, patch through your package manager against the distro's own security advisory below — it tracks the exact backported fix for your release, which can ship on a different timeline (and sometimes a different severity) than the upstream project.
This Important Server-Side Request Forgery (SSRF) vulnerability in MLflow does not affect Red Hat OpenShift AI (RHOAI) products as the vulnerable code is not present in the shipped components.
Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base, or stability.Source: Red Hat security advisory for CVE-2026-2393 (CC BY 4.0)
Frequently Asked Questions
Is CVE-2026-2393 in your dependencies?
Find it across PyPI, including transitive dependencies.