CVE-2026-2614
HighCVSS 7.5Exploitation Probability (EPSS)
High risk86th percentile - higher than 86% of all known CVEs
Summary
A vulnerability in mlflow/mlflow versions 3.9.0 and earlier allows an unauthenticated remote attacker to read arbitrary files from the server's filesystem. The issue occurs when a CreateModelVersion request includes the tag mlflow.prompt.is_prompt, which bypasses source path validation. The get_model_version_artifact_handler() function later uses this source to serve files without verifying the model version's prompt status, leading to a complete confidentiality compromise.
Risk Assessment
The organization is at risk of confidential data leakage, such as configuration files, credentials, or other sensitive information stored on the server. An attacker can access arbitrary files without authentication.
Recommendation
Immediately upgrade mlflow to version 3.10.0 or later, which contains the fix for this vulnerability. If an upgrade is not possible, restrict access to the mlflow API to trusted networks only.
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 vulnerability in the `_create_model_version()` handler of `mlflow/server/handlers.py` in mlflow/mlflow versions 3.9.0 and earlier allows an unauthenticated remote attacker to read arbitrary files from the server's filesystem. The issue arises when a `CreateModelVersion` request includes the tag `mlflow.prompt.is_prompt`, which bypasses source path validation. This enables an attacker to store an arbitrary local filesystem path as the model version source. The `get_model_version_artifact_handler()` function later uses this source to serve files without verifying the model version's prompt status, leading to a complete confidentiality compromise. This issue is fixed in version 3.10.0.