MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.13.0 until 3.15.0, LogInputs is absent from BEFORE_REQUEST_HANDLERS in the mlflow/server/auth package, allowing any authenticated user to call POST /api/2.0/mlflow/runs/log-inputs for another user's run_id and inject attacker-controlled DatasetInput records into the dataset_inputs lineage metadata without UPDATE 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, 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. 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's AI Gateway accepts an auth_config.api_base value when creating a gateway secret (mlflow/server/handlers.py, _create_gateway_secret) with no validation of scheme, host, or IP range; the value is stored verbatim. The gateway proxy endpoint (mlflow/server/gateway_api.py, raw_proxy) subsequently issues an HTTP request to that stored api_base plus a caller-supplied path and returns the full response body. MLflow's existing SSRF guard, _validate_webhook_url (which blocks non-global and metadata IPs), is never invoked anywhere in this gateway secret/proxy code path. The CreateGatewaySecret action additionally has no entry in the permission-validator map, so it requires only basic authentication rather than any specific scope, meaning any authenticated user — including read-only accounts — can create a secret pointing at an internal address and reach it via the proxy endpoint, potentially exposing cloud-instance IAM credentials via metadata services. This is related to CVE-2026-4035, which addresses a distinct mechanism in the same gateway-secret feature (server-side $ENV_VAR resolution inside the api_key field leaking credentials to the configured upstream); the finding here is an independent missing-validation gap in the api_base destination itself, unaffected by that fix.

A vulnerability has been found in MLflow up to 4666cffc7912ea606d592fc38d6a75e2935f65e7. The impacted element is an unknown function of the component Experiment-scoped Label Schema CRUD API. Such manipulation leads to missing authorization. It is possible to launch the attack remotely. A high complexity level is associated with this attack. The exploitability is regarded as difficult. The exploit has been disclosed to the public and may be used. A reply to the GitHub issue explains, that "[t]he labeling schema PR has not been merged yet. The auth handlers will be added before the release."