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, CreateModelVersion accepts a run_id or model_id after _validate_source_run() or _validate_source_model() in mlflow/server/handlers.py verifies only path containment, allowing authenticated users to create a model version that references another user's artifact directory and read files through GET /model-versions/get-artifact without the required READ permission. 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. Prior to 3.15.0, CreateModelVersion accepts a run_id or model_id after _validate_source_run() or _validate_source_model() in mlflow/server/handlers.py verifies only path containment, allowing authenticated users to create a model version that references another user's artifact directory and read files through GET /model-versions/get-artifact without the required READ permission. This issue is fixed in version 3.15.0.

Affected: npm: mlflow

### Summary

The `_validate_source_run` and `_validate_source_model` functions in `mlflow/server/handlers.py` verify that a model version source path is within the artifact directory of a specified run or logged model, but do not check whether the caller has READ permission on that run or model. An authenticated MLflow user can therefore reference another user's run_id in `CreateModelVersion`, creating a model version whose artifact URI points at the victim's artifact directory. If the calling user has MANAGE permission on the registered model (which they do after creation), they can then read arbitrary files from the victim's artifact directory via `GET /model-versions/get-artifact`, bypassing the experiment-level READ permission gate on `GET /get-artifact`.

### Details

`POST /api/2.0/mlflow/model-versions/create` is protected: the caller must have UPDATE permission on the registered model. However, the source/run_id validation performed inside `_validate_source_run` only verifies path containment, not caller authorization:

```python # mlflow/server/handlers.py _validate_source_run() def _validate_source_run(source: str, run_id: str) -> None: if is_local_uri(source): if run_id: store = _get_tracking_store() run = store.get_run(run_id) # <-- no permission check on run_id source = pathlib.Path(local_file_uri_to_path(source)).resolve() if is_local_uri(run.info.artifact_uri): run_artifact_d…

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