CVE-2026-2393

A vulnerability in MLflow versions up to 3.10.1.dev0 allows unauthorized access to multipart upload (MPU) endpoints when `--serve-artifacts` mode is enabled. The authorization logic does not enforce resource-level permission checks for `/mlflow-artifacts/mpu/*` endpoints, enabling attackers to overwrite artifacts belonging to other users. This can lead to unauthorized cross-user writes, model supply chain poisoning, and arbitrary code execution when compromised models are loaded.

In MLflow version 3.9.0, the MLflow Assistant feature introduced improper origin validation in its /ajax-api endpoints. This vulnerability allows a remote attacker to exploit cross-origin requests from a malicious webpage to interact with the MLflow Assistant running on a victim's local machine.

In mlflow/mlflow, the FastAPI job endpoints under `/ajax-api/3.0/jobs/*` are not protected by authentication or authorization when the `basic-auth` app is enabled. Even with basic authentication enabled, any network client can submit, read, search, and cancel jobs without credentials.

A command injection vulnerability exists in MLflow version 3.8.0 in the model serving container initialization code. The `_install_model_dependencies_to_env()` function directly interpolates dependencies from the `python_env.yaml` file into a shell command without sanitization, allowing an attacker to execute arbitrary commands by supplying a malicious model artifact.

A path traversal vulnerability exists in the `extract_archive_to_dir` function of mlflow before version 3.7.0. Lack of tar member path validation allows an attacker to overwrite arbitrary files or escalate privileges.

A vulnerability in MLflow's pyfunc extraction process allows arbitrary file writes due to improper handling of tar archive entries. The use of `tarfile.extractall` without path validation enables crafted tar.gz files containing `..` or absolute paths to escape the intended extraction directory.

MLflow contains a weak password requirements vulnerability that allows authentication bypass. Authentication is not required to exploit it; the flaw results from weak password requirements and can be leveraged to bypass authentication on the system.

MLflow's statsmodel flavor, versions 2.1.0 to 3.14.0, omits the MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False security control entirely in _load_model(), which allows a remote attacker to execute arbitrary code via a crafted MLmodel artifact.

MLflow's dspy flavor, versions >= 2.0, applies the MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False security control only when the model_path ends in .pkl, which allows a remote attacker to execute arbitrary code via a crafted MLmodel artifact.