Description (CNA)
A vulnerability in mlflow/mlflow versions prior to 3.11.0 allows for the resolution of environment variables in AI Gateway secrets, which can be exploited to exfiltrate sensitive server-side environment credentials to an attacker-controlled endpoint. This issue arises because the `api_key` field in gateway secrets can accept `$ENV_VAR` references, which are resolved against the MLflow server's environment during runtime. The resolved secrets are then sent in provider authentication headers to the configured upstream `api_base`. This vulnerability can be exploited by low-privileged authenticated users in basic-auth deployments or by unauthenticated users in default deployments without `basic-auth`. The impact includes potential leakage of sensitive credentials such as cloud artifact credentials (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`), which could lead to artifact poisoning and cross-boundary code execution in downstream environments. The issue is fixed in version 3.11.0.
Affected (CNA)
| Vendor / product | Versions |
|---|---|
| mlflow / mlflow/mlflow | affected unspecified before 3.11.0 |
CWE
CWE-201
References
- https://huntr.com/bounties/f8e591a0-0f19-4910-b82e-16c9956f2233
- https://github.com/mlflow/mlflow/commit/4a3f2f720cb4f058c9e0c5b883e0acc9ab64a7f3
ADP enrichment
No English description supplied.
A flaw was found in MLflow. This vulnerability allows an attacker to exfiltrate sensitive server-side environment credentials. It occurs because the AI Gateway secrets can resolve environment variables, which are then sent to an attacker-controlled endpoint. This could lead to unauthorized access to cloud resources and potentially enable cross-boundary code execution.