PYSEC-2026-1642 MLflow Weak Password Requirements Authentication Bypass Vulnerability

MLflow Weak Password Requirements Authentication Bypass Vulnerability. This vulnerability allows remote attackers to bypass authentication on affected installations of MLflow. Authentication is not required to exploit this vulnerability. The specific flaw exists within the handling of passwords...

PYSEC-2026-1646 MLflow Cross-Site Request Forgery (CSRF) vulnerability

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...

PYSEC-2026-1637 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...

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...

PYSEC-2026-1638 MLflow has a Local File Read/Path Traversal in dbfs

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...

PYSEC-2026-1658 MLflow Uncontrolled Resource Consumption vulnerability

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...

PYSEC-2026-1660 MLFlow unsafe deserialization

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 on an end user’s system when interacted with...

PYSEC-2026-1645 Undefined Behavior in mlflow

A vulnerability in mlflow/mlflow version 2.11.1 allows attackers to create multiple models with the same name by exploiting URL encoding. This flaw can lead to Denial of Service DoS as an authenticated user might not be able to use the intended model, as it will open a different model each time...

PYSEC-2026-1647 MLFlow unsafe deserialization

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...

PYSEC-2026-1657 MLFlow improper input validation

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 due to unfiltered input...

PYSEC-2026-1654 MLFlow unsafe deserialization

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...

PYSEC-2026-1636 MLFlow unsafe deserialization

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...

PYSEC-2026-1648 MLFlow unsafe deserialization

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...

PYSEC-2026-1643 MLFlow unsafe deserialization

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...

PYSEC-2026-1663 MLFlow unsafe deserialization

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...

PYSEC-2026-1651 MLFlow unsafe deserialization

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...

PYSEC-2026-1641 mlflow vulnerable to Path Traversal

A path traversal vulnerability exists in the mlflow/mlflow repository, specifically within the artifact deletion functionality. Attackers can bypass path validation by exploiting the double decoding process in the deleteartifactmlflowartifacts handler and localfileuritopath function, allowing for...

PYSEC-2026-1649 mlflow vulnerable to Path Traversal

A path traversal vulnerability exists in the mlflow/mlflow repository due to improper handling of URL parameters. By smuggling path traversal sequences using the ';' character in URLs, attackers can manipulate the 'params' portion of the URL to gain unauthorized access to files or directories. Th...

PYSEC-2026-1653 mlflow vulnerable to Path Traversal

A path traversal vulnerability exists in the createmodelversion function within server/handlers.py of the mlflow/mlflow repository, due to improper validation of the source parameter. Attackers can exploit this vulnerability by crafting a source parameter that bypasses the...

PYSEC-2026-1655 mlflow vulnerable to Path Traversal

A path traversal vulnerability exists in the mlflow/mlflow repository, specifically within the handling of the artifactlocation parameter when creating an experiment. Attackers can exploit this vulnerability by using a fragment component in the artifact location URI to read arbitrary files on the...