Cross-Site Request Forgery (CSRF)

mlflow is vulnerable to Cross-Site Request Forgery CSRF. The vulnerability is due to lack of proper protection mechanisms in the Signup feature, allowing an attacker to craft malicious requests to create an account and perform unauthorized actions...

CVE-2025-1473

A Cross-Site Request Forgery CSRF vulnerability exists in the Signup feature of mlflow/mlflow versions 2.17.0 to 2.20.1. This vulnerability allows an attacker to create a new account, which may be used to perform unauthorized actions on behalf of the malicious user...

CVE-2024-6838

In mlflow/mlflow version v2.13.2, a vulnerability exists that allows the creation or renaming of an experiment with a large number of integers in its name due to the lack of a limit on the experiment name. This can cause the MLflow UI panel to become unresponsive, leading to a potential denial of...

GHSA-4RJ2-9GCX-5QHX MLflow has Weak Password Requirements

In mlflow/mlflow version 2.18, an admin is able to create a new user account without setting a password. This vulnerability could lead to security risks, as accounts without passwords may be susceptible to unauthorized access. Additionally, this issue violates best practices for secure user accou...

MLflow Uncontrolled Resource Consumption vulnerability

In mlflow/mlflow version 2.17.2, the /graphql endpoint is vulnerable to a denial of service attack. An attacker can create large batches of queries that repeatedly request all runs from a given experiment. This can tie up all the workers allocated by MLFlow, rendering the application unable to...

Missing Input Length Validation

Overview mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. Affected versions of this package are vulnerable to Missing Input Length Validation in the experimentname - passed to...

Relative Path Traversal

Overview mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. Affected versions of this package are vulnerable to Relative Path Traversal in the...

PYSEC-2025-17

In mlflow/mlflow version 2.18, an admin is able to create a new user account without setting a password. This vulnerability could lead to security risks, as accounts without passwords may be susceptible to unauthorized access. Additionally, this issue violates best practices for secure user accou...

CVE-2025-0453 Denial of Service through Batched Queries in GraphQL in mlflow/mlflow

In mlflow/mlflow version 2.17.2, the /graphql endpoint is vulnerable to a denial of service attack. An attacker can create large batches of queries that repeatedly request all runs from a given experiment. This can tie up all the workers allocated by MLFlow, rendering the application unable to...

CVE-2025-0453 Denial of Service through Batched Queries in GraphQL in mlflow/mlflow

In mlflow/mlflow version 2.17.2, the /graphql endpoint is vulnerable to a denial of service attack. An attacker can create large batches of queries that repeatedly request all runs from a given experiment. This can tie up all the workers allocated by MLFlow, rendering the application unable to...

CVE-2025-1473 CSRF 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. This vulnerability allows an attacker to create a new account, which may be used to perform unauthorized actions on behalf of the malicious user...

CVE-2025-1473

In MLflow (mlflow/mlflow), a CSRF vulnerability affects versions 2.17.0 to 2.20.1 in the Signup feature, allowing an attacker to create a new account and potentially perform unauthorized actions on behalf of the attacker’s account. The CVE-2025-1473 entry documents the flaw and its impact as Cros...

CVE-2024-8859 Path Traversal in mlflow/mlflow

A path traversal vulnerability exists in mlflow/mlflow version 2.15.1. When users configure and use the dbfs service, concatenating the URL directly into the file protocol results in an arbitrary file read vulnerability. This issue occurs because only the path part of the URL is checked, while...

CVE-2024-6838 Uncontrolled Resource Consumption in mlflow/mlflow

In mlflow/mlflow version v2.13.2, a vulnerability exists that allows the creation or renaming of an experiment with a large number of integers in its name due to the lack of a limit on the experiment name. This can cause the MLflow UI panel to become unresponsive, leading to a potential denial of...

PT-2025-12315

Name of the Vulnerable Software and Affected Versions mlflow/mlflow version 2.17.2 Description The /graphql endpoint is vulnerable to a denial of service attack. An attacker can create large batches of queries that repeatedly request all runs from a given experiment, tying up all the workers...

PT-2025-12167

Name of the Vulnerable Software and Affected Versions mlflow/mlflow version v2.13.2 Description A potential denial of service issue exists due to the lack of a limit on the experiment name, allowing the creation or renaming of an experiment with a large number of integers in its name. This can...

CVE-2024-3573

mlflow/mlflow is vulnerable to Local File Inclusion LFI due to improper parsing of URIs, allowing attackers to bypass checks and read arbitrary files on the system. The issue arises from the 'islocaluri' function's failure to properly handle URIs with empty or 'file' schemes, leading to the...

CVE-2024-27132

Insufficient sanitization in MLflow leads to XSS when running an untrusted recipe. This issue leads to a client-side RCE when running an untrusted recipe in Jupyter Notebook. The vulnerability stems from lack of sanitization over template variables...

CVE-2024-27133

Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset. This issue leads to a client-side RCE when running the recipe in Jupyter Notebook. The vulnerability stems from lack of sanitization over dataset table fields...

CVE-2024-37053

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...