MLflow vulnerable to remote code execution on unauthenticated servers via third-party scorer deserialization importing caller-placed artifact modules

## Summary MLflow 3.12.0 introduced registration and deserialization support for third-party scorers in OSS MLflow. During scorer deserialization, the server accepts a request-controlled Python module path beneath one of four allowed package prefixes, imports that module, obtains a request-controlled class, and instantiates it synchronously. An HTTP caller can combine this behavior with MLflow's built-in experiment, run, and artifact-upload endpoints: 1. Create an experiment whose local artifact location points to a writable allowed scorer package. 2. Create a run, causing MLflow to allocate `<artifact_location>/<run_id>/artifacts/`. 3. Upload a Python module into that run artifact directory. 4. Submit a serialized third-party scorer whose module path resolves to the uploaded file. 5. Trigger server-side import and class construction through `/ajax-api/3.0/mlflow/scorer/invoke`. On the default no-auth server using the official MLflow v3.14.0 image, this produces unauthenticated arbitrary Python code execution with the privileges of the MLflow server process. The reproduced image runs the process as uid 0 and permits writes to its installed Python package tree. The attached proof of concept is intentionally harmless. The uploaded constructor performs one action only: it writes a random marker beside its own uploaded run artifact. The marker is then downloaded through MLflow's run-artifact endpoint and checked for an exact match. ## Technical details ### 1. The allow-list accepts attacker-created descendants At release commit `86cd7f5a1dc25a1387ad07c87811bbc30f62951c`, `mlflow/genai/scorers/scorer_utils.py` defines these allowed roots: ```python THIRD_PARTY_SCORER_ALLOWED_MODULES = frozenset({ "mlflow.genai.scorers.ragas", "mlflow.genai.scorers.deepeval", "mlflow.genai.scorers.trulens", "mlflow.genai.scorers.phoenix", }) ``` In `mlflow/genai/scorers/base.py`, the deserializer accepts either an exact root or any descendant: ```python module_path == m or module_path.startswith(m + ".") ``` It then performs the following operations using request-controlled values: ```python module = importlib.import_module(module_path) scorer_class = getattr(module, class_name, None) scorer_instance = scorer_class(**init_kwargs) ``` A package-name prefix is not a provenance boundary. If the server process can write below an allowed package, an attacker-created descendant can satisfy the string-prefix check and become importable Python code. Relevant source locations at the v3.14.0 release commit: * `mlflow/genai/scorers/scorer_utils.py`, allowed third-party scorer modules * `mlflow/genai/scorers/base.py`, third-party scorer deserialization and construction ### 2. Deserialization occurs before trace processing In `mlflow/server/handlers.py`, `_invoke_scorer_handler()` parses the supplied serialized scorer and calls: ```python scorer = Scorer.model_validate_json(serialized_scorer) ``` Only after deserialization and construction does the handler call: ```python batches = get_trace_batches_for_scorer(trace_ids, scorer, tracking_store) ``` The attacker-controlled constructor therefore runs before the server validates or resolves the supplied trace IDs. The minimal proof class is deliberately not a complete MLflow scorer. After its constructor writes the marker, later trace-batching logic accesses scorer attributes that the proof class does not implement. The resulting HTTP 500 occurs after code execution and does not invalidate the proof. No background job execution or special job-execution server option is required to reach the constructor. ### 3. MLflow's artifact layout places the module under the trusted prefix The experiment creation endpoint accepts an `artifact_location`. For file-backed experiments, MLflow derives each run artifact directory as: ```text <artifact_location>/<run_id>/artifacts/ ``` The proof of concept selects this package directory in the official v3.14.0 image: ```text file:///usr/local/lib/python3.10/site-packages/mlflow/genai/scorers/trulens ``` After creating a run and uploading `audit_<token>.py`, the resulting filesystem location is: ```text /usr/local/lib/python3.10/site-packages/mlflow/genai/scorers/trulens/<run_id>/artifacts/audit_<token>.py ``` The corresponding import path is: ```text mlflow.genai.scorers.trulens.<run_id>.artifacts.audit_<token> ``` The attack does not overwrite an existing package module. It creates a new run-scoped module in the normal file-backed artifact layout. The run ID is returned to the caller. ### 4. Authentication and authorization behavior With authentication disabled, as in the default reproduced server configuration, no credentials are required. When MLflow basic authentication is enabled, authentication still applies. However, the scorer-invocation AJAX endpoint is assigned to `validate_gateway_proxy`, which returns `True` without performing an experiment- or scorer-level resource permission check. Practical exploitation in an authenticated deployment still requires the caller to create an experiment and run and upload artifacts to them. The key authorization issue is that scorer deserialization itself is not gated by a resource-specific permission check for the supplied experiment before the import occurs. ## Proof of concept The attached reproduction is intentionally isolated: * exact official MLflow v3.14.0 image pinned by manifest digest; * Docker network disabled; * MLflow bound to container loopback; * no shell command or external callback; * no external request; * no host filesystem write; * marker created beside the uploaded run artifact; * marker retrieved through the run-artifact download endpoint; and * disposable container removed after completion. Run: ```bash ./run-official-image.sh ``` Expected output: ```text MLFLOW_VERSION=3.14.0 PACKAGE_DIR=/usr/local/lib/python3.10/site-packages/mlflow/genai/scorers/trulens CREATE_EXPERIMENT_STATUS=200 CREATE_RUN_STATUS=200 UPLOAD_STATU…