MLflow Experiment-scoped Label Schema CRUD API authorization

Command Injection in mlflow/mlflow

A vulnerability in mlflow/mlflow version 8.2.1 allows for remote code execution due to improper neutralization of special elements used in an OS command ('Command Injection')…

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…

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…

Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.5.0 or newer, enabling a maliciously uploaded PyTorch model to run arbitrary code…

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…

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…

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…

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…

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…

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…

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…

This vulnerability is capable of writing arbitrary files into arbitrary locations on the remote filesystem in the context of the server process

with only one user interaction(download a malicious config), attackers can gain full command execution on the victim system

Path Traversal in GitHub repository mlflow/mlflow prior to 2.9.2

Improper Neutralization of Special Elements Used in a Template Engine in GitHub repository mlflow/mlflow prior to 2.9.2

Authentication Bypass in mlflow/mlflow

Authorization Bypass in mlflow/mlflow

DNS Rebinding Vulnerability in mlflow/mlflow

A path traversal vulnerability exists in the mlflow/mlflow repository, specifically within the artifact deletion functionality

Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2

Incomplete Fix for CVE-2025-10279: Insecure Temporary Directory Permissions in mlflow/mlflow

Command Injection in mlflow/mlflow

OS Command Injection in GitHub repository mlflow/mlflow prior to 2.6.0

Environment Variable Resolution Vulnerability in mlflow/mlflow

Arbitrary File Read via Prompt Tag Source Validation Bypass in mlflow/mlflow

In mlflow/mlflow version 2.17.2, the `/graphql` endpoint is vulnerable to a denial of service attack

A path traversal vulnerability exists in mlflow/mlflow version 2.15.1

A Local File Inclusion (LFI) vulnerability was identified in mlflow/mlflow, specifically in version 2.9.2, which was fixed in version 2.11.3

A path traversal vulnerability exists in mlflow/mlflow version 2.11.0, identified as a bypass for the previously addressed CVE-2023-6909

A path traversal vulnerability exists in the mlflow/mlflow repository, specifically within the handling of the `artifact_location` parameter when creating an experiment

A path traversal vulnerability exists in the mlflow/mlflow repository due to improper handling of URL parameters

A path traversal vulnerability exists in the `_create_model_version()` function within `server/handlers.py` of the mlflow/mlflow repository, due to improper validation of the…

A path traversal vulnerability exists in mlflow/mlflow version 2.9.2, allowing attackers to access arbitrary files on the server

This vulnerability enables malicious users to read sensitive files on the server

Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2

An issue in MLFlow versions 2.8.1 and before allows a remote attacker to obtain sensitive information via a crafted request to REST API

MLflow allowed arbitrary files to be PUT onto the server

A directory traversal vulnerability in the /get-artifact API method of the mlflow platform up to v2.0.1 allows attackers to read arbitrary files on the server via the path…

Relative Path Traversal in GitHub repository mlflow/mlflow prior to 2.3.1

Insecure Temporary File in GitHub repository mlflow/mlflow prior to 1.23.1

Server-Side Request Forgery (SSRF) in mlflow/mlflow

Unauthorized Access to Tracing and Assessment Endpoints in mlflow/mlflow

A Cross-Site Request Forgery (CSRF) vulnerability exists in the Signup feature of mlflow/mlflow versions 2.17.0 to 2.20.1

Privilege Escalation in mlflow/mlflow

Excessive directory permissions in MLflow leads to local privilege escalation when using spark_udf