Affected: Bitnami: mlflow

MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. Prior to 3.15.0, the unauthenticated POST /api/2.0/mlflow/webhooks/{id}/test endpoint calls _validate_webhook_url() in mlflow/utils/validation.py only for the original URL while mlflow/webhooks/delivery.py follows redirects and re-resolves the hostname without pinning the validated address, allowing attackers to reach internal or cloud metadata services and receive response_status and response_body. This issue is fixed in version 3.15.0.

MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. Starting in 3.3.0 and prior to 3.15.0, the unauthenticated POST /api/2.0/mlflow/webhooks/{id}/test endpoint calls _validate_webhook_url() in mlflow/utils/validation.py only for the original URL while mlflow/webhooks/delivery.py follows redirects and re-resolves the hostname without pinning the validated address, allowing attackers to reach internal or cloud metadata services and receive response_status and response_body. This issue is fixed in version 3.15.0.

Affected: PyPI: mlflow

### Summary The default MLflow Tracking Server (`mlflow server`, no authentication, default SQLite backend) exposes the model-registry webhooks API unauthenticated, including a synchronous `POST /api/2.0/mlflow/webhooks/{id}/test` endpoint that returns the upstream response status and body to the caller. The SSRF guard added in PR #20747 (`_validate_webhook_url`, shipped in 3.10.0) resolves the webhook hostname and rejects non-public IPs, but it is bypassable: delivery follows HTTP redirects (no `allow_redirects=False`) and never pins the validated IP. An attacker hosts a public HTTPS endpoint that passes the guard and returns `302 Location: http://169.254.169.254/...` (or `http://127.0.0.1:...`); MLflow follows it and never re-validates the redirect target. Because `/test` reflects the response body, this is an unauthenticated full-read SSRF on a default server.

### Details Three facts combine:

1. Webhook endpoints are unauthenticated on a default server. The only webhook authorization lives in the optional auth plugin (`mlflow/server/auth/__init__.py`, `WEBHOOK_BEFORE_REQUEST_HANDLERS`), which is not loaded by default.

2. The guard validates but pins nothing — `mlflow/utils/validation.py` `_validate_webhook_url`: ```python schemes = _MLFLOW_WEBHOOK_ALLOWED_SCHEMES.get() # default ["https"] if parsed_url.scheme not in schemes: raise ... if not _MLFLOW_WEBHOOK_ALLOW_PRIVATE_IPS.get(): # default False for addr_info in socket.getaddrinfo(hostname, None):…

Affected: PyPI: mlflow

### Summary The default MLflow Tracking Server (`mlflow server`, no authentication, default SQLite backend) exposes the model-registry webhooks API unauthenticated, including a synchronous `POST /api/2.0/mlflow/webhooks/{id}/test` endpoint that returns the upstream response status and body to the caller. The SSRF guard added in PR #20747 (`_validate_webhook_url`, shipped in 3.10.0) resolves the webhook hostname and rejects non-public IPs, but it is bypassable: delivery follows HTTP redirects (no `allow_redirects=False`) and never pins the validated IP. An attacker hosts a public HTTPS endpoint that passes the guard and returns `302 Location: http://169.254.169.254/...` (or `http://127.0.0.1:...`); MLflow follows it and never re-validates the redirect target. Because `/test` reflects the response body, this is an unauthenticated full-read SSRF on a default server.

### Details Three facts combine:

1. Webhook endpoints are unauthenticated on a default server. The only webhook authorization lives in the optional auth plugin (`mlflow/server/auth/__init__.py`, `WEBHOOK_BEFORE_REQUEST_HANDLERS`), which is not loaded by default.

2. The guard validates but pins nothing — `mlflow/utils/validation.py` `_validate_webhook_url`: ```python schemes = _MLFLOW_WEBHOOK_ALLOWED_SCHEMES.get() # default ["https"] if parsed_url.scheme not in schemes: raise ... if not _MLFLOW_WEBHOOK_ALLOW_PRIVATE_IPS.get(): # default False for addr_info in socket.getaddrinfo(hostname, None):…

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Ein entfernter, anonymer Angreifer kann eine Schwachstelle in MLflow ausnutzen, um Sicherheitsvorkehrungen zu umgehen und Daten offenzulegen oder zu manipulieren.