CVE-2026-0545 — mlflow
CRITICALCVE-2026-0545 is a critical-severity (CVSS 9.1) Missing Authentication vulnerability in mlflow. The records list no fixed version yet; check the vendor advisory below and apply the mitigations in the meantime.
mlflow: FastAPI job endpoints under `/ajax-api/3.0/jobs/*` are not protected by authentication or authorization
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.
- CISA assesses this as automatable — exploitation doesn’t require manual, per-target effort, which raises the odds of mass scanning and opportunistic attacks.
Exploitation and automatability from CISA’s SSVC triage for CVE-2026-0545.
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
In mlflow/mlflow, the FastAPI job endpoints under /ajax-api/3.0/jobs/* are not protected by authentication or authorization when the basic-auth app is enabled. This vulnerability affects the latest version of the repository. If job execution is enabled (MLFLOW_SERVER_ENABLE_JOB_EXECUTION=true) and any job function is allowlisted, any network client can submit, read, search, and cancel jobs without credentials, bypassing basic-auth entirely. This can lead to unauthenticated remote code execution if allowed jobs perform privileged actions such as shell execution or filesystem changes. Even if jobs are deemed safe, this still constitutes an authentication bypass, potentially resulting in job spam, denial of service (DoS), or data exposure in job results.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | mlflow | all versions | No fix |
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.
Remediation status
The records we use list no patched version of mlflow for CVE-2026-0545. Check the maintainer's advisory, and where your build allows, override or pin the dependency away from the vulnerable range.
Mitigate without a patch
Put an independent control in front of the weakness: restrict the affected endpoint or interface to trusted networks, require an additional authentication factor or proxy-level check, and invalidate existing sessions and credentials in case the flaw has already been used.
How to detect CVE-2026-0545
A community-maintained Nuclei template exists for this CVE. You can scan for it directly:
nuclei -id cve-2026-0545 -u https://target- Template
- MLflow Job API - Authentication Bypass
- Severity
- critical
- Impact
- Unauthenticated attackers can execute jobs remotely, potentially leading to remote code execution, denial of service, or data exposure.
- Remediation
- Update to the latest version with fixed authentication enforcement on job endpoints.
Template by ProjectDiscovery nuclei-templates (DhiyaneshDk), MIT licensed. View the full template. Scan only systems you are authorised to test.
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 flaw in MLflow allows unauthenticated access to FastAPI job endpoints when `basic-auth` is enabled, bypassing security controls. A remote attacker could exploit this to achieve unauthenticated remote code execution if job execution is enabled and configured for privileged operations. This also poses…
To mitigate this issue, disable job execution on the MLflow server by setting the `MLFLOW_SERVER_ENABLE_JOB_EXECUTION` environment variable to `false`. This prevents unauthenticated access to job endpoints. Example: `MLFLOW_SERVER_ENABLE_JOB_EXECUTION=false mlflow server` If configured as a service, update the service definition to include this environment variable. A restart of the MLflow server is required for the change to take effect.Source: Red Hat security advisory for CVE-2026-0545 (CC BY 4.0)
| Product | Fixed in | Advisory |
|---|---|---|
| Red Hat OpenShift AI 3.4 | rhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9:1787073866 | RHSA-2026:60520 |
Frequently Asked Questions
Is CVE-2026-0545 in your dependencies?
Find it across PyPI, including transitive dependencies.