LF Projects MLflow

Absolute Path Traversal in mlflow/mlflow

MLflow Arbitrary File Write

MLflow Arbitrary File Upload

Relative Path Traversal in mlflow/mlflow

Path Traversal: '\..\filename'

Remote Code Execution due to Full Controlled File Write in mlflow/mlflow

Command Injection in mlflow/mlflow

Improper Neutralization of Special Elements Used in a Template Engine in mlflow/mlflow

Path Traversal Vulnerability in mlflow/mlflow

Path Traversal: '\..\filename' in mlflow/mlflow

MLflow Tracking Server Model Creation Directory Traversal Remote Code Execution Vulnerability

Path Traversal: '\..\filename' in mlflow/mlflow

Missing Authentication for Critical Function in mlflow/mlflow

Path Traversal: '\..\filename'

Server-Side Request Forgery (SSRF)

MLflow Weak Password Requirements Authentication Bypass Vulnerability

MLflow Authentication Bypass

Command Injection in mlflow/mlflow

Path Traversal in mlflow/mlflow

Insufficient sanitization in MLflow leads to XSS when running an untrusted recipe.

Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset.

Improper Origin Validation in mlflow/mlflow

MLflow: Unauthenticated full-read SSRF in webhook delivery: _validate_webhook_url bypassed via unvalidated HTTP redirects (and DNS rebinding)

Local File Inclusion (LFI) via Scheme Confusion in mlflow/mlflow

Path Traversal Vulnerability in mlflow/mlflow

Environment Variable Resolution Vulnerability in mlflow/mlflow

Command Injection

Missing Authorization Validation in mlflow/mlflow

Command Injection in mlflow/mlflow

OS Command Injection in mlflow/mlflow

Unrestricted Upload of File with Dangerous Type

Remote Code Execution can occur in versions of the MLflow platform running version 1.11.0 or newer, enabling a maliciously crafted MLproject to execute arbitrary code on an end user’s system when run.

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.27.0 or newer, enabling a maliciously crafted Recipe to execute arbitrary code on an end user’s system when run.

Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s system when interacted with.

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with.

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.24.0 or newer, enabling a maliciously uploaded pmdarima model to run arbitrary code on an end user’s system when interacted with.

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with.

Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.23.0 or newer, enabling a maliciously uploaded LightGBM scikit-learn model to run arbitrary code on an end user’s system when interacted with.

Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.0.0rc0 or newer, enabling a maliciously uploaded Tensorflow model to run arbitrary code on an end user’s system when interacted with.

Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.5.0 or newer, enabling a maliciously uploaded Langchain AgentExecutor model to run arbitrary code on an end user’s system when interacted with.