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.