Affected: Bitnami: mlflow

Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.2.1.

Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.2.1.

Affected: PyPI: mlflow

### 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 2.2.1 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 detailed in https://nvd.nist.gov/vuln/detail/CVE-2023-1177 enables an actor to download arbitrary files unrelated to MLflow from the host server, including any files stored in remote locations to which the host server has access.

### Patches This vulnerability has been patched in MLflow 2.2.1, which was released to PyPI on March 2nd, 2023. If you are using `mlflow server` or `mlflow ui` with the MLflow Model Registry, we recommend upgrading to MLflow 2.2.1 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 VP…

Affected: PyPI: mlflow

Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.2.1.