GHSA-g6pg-52vf-843h CVE-2025-15381 PYSEC-2026-2657
MLFlow allows Tracing + Assessments Access
- Severity
- 8.1 / 10
- High · CVSS 3.0
- Exploit chance (EPSS)
- 0.34%
- Chance of exploitation in the next 30 days. Higher than 27.96% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-200 Exposure of Sensitive Information to an Unauthorized Actor
- Fix
- None yet
- No fixed version published
Description
In the latest version of mlflow/mlflow, when the basic-auth app is enabled, tracing and assessment endpoints are not protected by permission validators. This allows any authenticated user, including those with NO_PERMISSIONS on the experiment, to read trace information and create assessments for traces they should not have access to. This vulnerability impacts confidentiality by exposing trace metadata and integrity by allowing unauthorized creation of assessments. Deployments using mlflow server --app-name=basic-auth are affected.
Affected versions
PyPI
| Package | Vulnerable | Fixed in |
|---|---|---|
| mlflow | <= 3.8.1 |
No fix yet |
How to fix
No fixed version has been published yet. Until one is, remove or replace the package, or turn off or isolate the affected feature. This page updates when a fix ships.
Severity breakdown
CVSS 3.0 8.1
CVSS:3.0/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N
- Attack vector
- Network
- Attack complexity
- Low
- Privileges required
- Low
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- High
- Availability
- None
Weakness
CWE-200: Exposure of Sensitive Information to an Unauthorized Actor
The product exposes sensitive information to an actor that is not explicitly authorized to have access to that information.
How to prevent it
- Architecture and Design: Compartmentalize the system to have "safe" areas where trust boundaries can be unambiguously drawn. Do not allow sensitive data to go outside of the trust boundary and always be careful when interfacing with a compartment outside of the safe area. Ensure that appropriate compartmentalization is built into the system design, and the compartmentalization allows for and reinforces privilege separation functionality. Architects and designers should rely on the principle of least privilege to decide the appropriate time to use privileges and the time to drop privileges.
References
Advisories
Packages
Other links
Timeline
- Published
- Last updated by the source
- Latest EPSS score
Related advisories
| Advisory | Severity | Why it's related |
|---|---|---|
| GHSA-7gwp-5pfp-969j MLflow: Unauthenticated full-read SSRF in webhook delivery: _validate_webhook_url bypassed via unvalidated HTTP redirects (and DNS rebinding) | Critical Known exploited | Also affects mlflow |
| GHSA-26p8-2jq9-3vq9 MLflow vulnerable to remote code execution on unauthenticated servers via third-party scorer deserialization importing caller-placed artifact modules | Critical | Also affects mlflow |
| GHSA-j7qq-vr8c-xvw3 MLflow CrewAI autologging records LLM provider API keys in logged trace spans | Medium | Also affects mlflow |
| GHSA-m65p-9j7q-6w33 MLflow vulnerable to arbitrary file read on unauthenticated servers via prompt model version source | High | Also affects mlflow |
| GHSA-2wqp-q3c6-ccj5 MLflow: SSRF on unauthenticated MLflow servers via third-party scorer kwargs in the scorer invoke, register, and GenAI evaluate endpoints | Medium | Also affects mlflow |
| GHSA-wqmh-qccw-pqwf MLflow FileStore trace IDs allow path traversal and arbitrary file writes | High | Also affects mlflow |
| GHSA-gqvg-gmmx-x4hm MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False safety control bypassed by mlflow.statsmodels flavor — RCE via crafted model artifact | High | Also affects mlflow |
| GHSA-h7x2-h6g9-p789 MLflow AI Gateway permits SSRF through an unvalidated api_base | High | Also affects mlflow |
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