CVE-2026-0596
HighCVSS 7.8Exploitation Probability (EPSS)
Elevated risk68th percentile - higher than 68% of all known CVEs
Summary
A command injection vulnerability exists in mlflow/mlflow when serving a model with `enable_mlserver=True`. The `model_uri` is embedded directly into a shell command executed via `bash -c` without proper sanitization, allowing an attacker to execute arbitrary commands.
Risk Assessment
An attacker can exploit this vulnerability for privilege escalation if a higher-privileged service serves models from a directory writable by lower-privileged users, potentially leading to full system compromise.
Recommendation
Immediately update mlflow/mlflow to the latest patched version and avoid using `enable_mlserver=True` with untrusted model sources. Additionally, apply proper input sanitization and restrict permissions on model directories.
Other vulnerabilities in mlflow
See all- CVE-2026-2393High
A Server-Side Request Forgery (SSRF) vulnerability exists in MLflow versions prior to 3.9.0. The `_create_webhook()` function accepts a user-controlled `url` parameter without validation, allowing an attacker to force the MLflow backend to send HTTP requests to internal services or arbitrary external servers.
- CVE-2026-64849CriticalActively exploited
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.
- CVE-2026-2651Critical
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.
- CVE-2026-2611Critical
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.
- CVE-2026-0545Critical
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.
- CVE-2025-15379Critical
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.
- CVE-2025-15036Critical
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.
- CVE-2025-15031Critical
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.
- CVE-2026-69148High
MLflow prior to 3.15.0 allows authenticated users to create a model version referencing another user's artifact directory and read files without the required READ permission.
- CVE-2026-69146Medium
MLflow is an open source AI engineering platform. 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.
Original NVD description (English source)
A command injection vulnerability exists in mlflow/mlflow when serving a model with `enable_mlserver=True`. The `model_uri` is embedded directly into a shell command executed via `bash -c` without proper sanitization. If the `model_uri` contains shell metacharacters, such as `$()` or backticks, it allows for command substitution and execution of attacker-controlled commands. This vulnerability affects the latest version of mlflow/mlflow and can lead to privilege escalation if a higher-privileged service serves models from a directory writable by lower-privileged users.