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Security Advisories: GSA_kwCzR0hTQS04M2ZtLXc3OW0tNjRyNc4AAzCj
Remote file access vulnerability in `mlflow server` and `mlflow ui` CLIs
Impact
Users of the MLflow Open Source Project who are hosting the MLflow Model Registry using the mlflow server
or mlflow ui
commands using an MLflow version older than MLflow 2.3.1 may be vulnerable to a remote file access exploit if they are not limiting who can query their server (for example, by using a cloud VPC, an IP allowlist for inbound requests, or authentication / authorization middleware).
This issue only affects users and integrations that run the mlflow server
and mlflow ui
commands. Integrations that do not make use of mlflow server
or mlflow ui
are unaffected; for example, the Databricks Managed MLflow product and MLflow on Azure Machine Learning do not make use of these commands and are not impacted by these vulnerabilities in any way.
The vulnerability is very similar to https://nvd.nist.gov/vuln/detail/CVE-2023-1177, and a separate CVE will be published and updated here shortly.
Patches
This vulnerability has been patched in MLflow 2.3.1, which was released to PyPI on April 27th, 2023. If you are using mlflow server
or mlflow ui
with the MLflow Model Registry, we recommend upgrading to MLflow 2.3.1 as soon as possible.
Workarounds
If you are using the MLflow open source mlflow server
or mlflow ui
commands, we strongly recommend limiting who can access your MLflow Model Registry and MLflow Tracking servers using a cloud VPC, an IP allowlist for inbound requests, authentication / authorization middleware, or another access restriction mechanism of your choosing.
If you are using the MLflow open source mlflow server
or mlflow ui
commands, we also strongly recommend limiting the remote files to which your MLflow Model Registry and MLflow Tracking servers have access. For example, if your MLflow Model Registry or MLflow Tracking server uses cloud-hosted blob storage for MLflow artifacts, make sure to restrict the scope of your server's cloud credentials such that it can only access files and directories related to MLflow.
Permalink: Referenceshttps://github.com/advisories/GHSA-83fm-w79m-64r5
JSON: https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS04M2ZtLXc3OW0tNjRyNc4AAzCj
Source: GitHub Advisory Database
Origin: Unspecified
Severity: Critical
Classification: General
Published: over 1 year ago
Updated: over 1 year ago
Identifiers: GHSA-83fm-w79m-64r5
References:
- https://github.com/mlflow/mlflow/security/advisories/GHSA-83fm-w79m-64r5
- https://github.com/advisories/GHSA-83fm-w79m-64r5
Blast Radius: 0.0
Affected Packages
pypi:mlflow
Dependent packages: 360Dependent repositories: 5,089
Downloads: 16,735,220 last month
Affected Version Ranges: < 2.3.1
Fixed in: 2.3.1
All affected versions: 0.0.1, 0.1.0, 0.2.0, 0.2.1, 0.3.0, 0.4.0, 0.4.1, 0.4.2, 0.5.0, 0.5.1, 0.5.2, 0.6.0, 0.7.0, 0.8.0, 0.8.1, 0.8.2, 0.9.0, 0.9.1, 1.0.0, 1.1.0, 1.2.0, 1.3.0, 1.4.0, 1.5.0, 1.6.0, 1.7.0, 1.7.1, 1.7.2, 1.8.0, 1.9.0, 1.9.1, 1.10.0, 1.11.0, 1.12.0, 1.12.1, 1.13.1, 1.14.0, 1.14.1, 1.15.0, 1.16.0, 1.17.0, 1.18.0, 1.19.0, 1.20.0, 1.20.1, 1.20.2, 1.21.0, 1.22.0, 1.23.0, 1.23.1, 1.24.0, 1.25.0, 1.25.1, 1.26.0, 1.26.1, 1.27.0, 1.28.0, 1.29.0, 1.30.0, 1.30.1, 2.0.0, 2.0.1, 2.1.0, 2.1.1, 2.2.0, 2.2.1, 2.2.2, 2.3.0
All unaffected versions: 2.3.1, 2.3.2, 2.4.0, 2.4.1, 2.4.2, 2.5.0, 2.6.0, 2.7.0, 2.7.1, 2.8.0, 2.8.1, 2.9.0, 2.9.1, 2.9.2, 2.10.0, 2.10.1, 2.10.2, 2.11.0, 2.11.1, 2.11.2, 2.11.3, 2.11.4, 2.12.0, 2.12.1, 2.12.2, 2.13.0, 2.13.1, 2.13.2, 2.14.0, 2.14.1, 2.14.2, 2.14.3, 2.15.0, 2.15.1, 2.16.0, 2.16.1, 2.16.2, 2.17.0, 2.17.1, 2.17.2, 2.18.0