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updated 1 month, 2 weeks ago by @LeSuisse Activity log
  • Created suggestion
  • @LeSuisse ignored
    4 packages
    • python312Packages.sagemaker-mlflow
    • python313Packages.sagemaker-mlflow
    • python314Packages.sagemaker-mlflow
    • pkgsRocm.python3Packages.sagemaker-mlflow
  • @LeSuisse accepted
  • @LeSuisse published on GitHub
Stored XSS via unsafe YAML parsing in MLflow

MLflow is vulnerable to Stored Cross-Site Scripting (XSS) caused by unsafe parsing of YAML-based MLmodel artifacts in its web interface. An authenticated attacker can upload a malicious MLmodel file containing a payload that executes when another user views the artifact in the UI. This allows actions such as session hijacking or performing operations on behalf of the victim. This issue affects MLflow version through 3.10.1

Affected products

mlfolw
  • =<3.10.1

Matching in nixpkgs

pkgs.mlflow-server

Open source platform for the machine learning lifecycle

Ignored packages (4)

Package maintainers

updated 1 month, 2 weeks ago by @LeSuisse Activity log
  • Created suggestion
  • @LeSuisse ignored
    4 packages
    • python312Packages.sagemaker-mlflow
    • python313Packages.sagemaker-mlflow
    • python314Packages.sagemaker-mlflow
    • pkgsRocm.python3Packages.sagemaker-mlflow
  • @LeSuisse accepted
  • @LeSuisse published on GitHub
Authorization Bypass in MLflow AJAX Endpoint

MLflow is vulnerable to an authorization bypass affecting the AJAX endpoint used to download saved model artifacts. Due to missing access‑control validation, a user without permissions to a given experiment can directly query this endpoint and retrieve model artifacts they are not authorized to access. This issue affects MLflow version through 3.10.1

Affected products

mlflow
  • =<3.10.1

Matching in nixpkgs

pkgs.mlflow-server

Open source platform for the machine learning lifecycle

Ignored packages (4)

Package maintainers

updated 2 months, 4 weeks ago by @LeSuisse Activity log
  • Created suggestion
  • @LeSuisse ignored
    4 packages
    • pkgsRocm.python3Packages.sagemaker-mlflow
    • python314Packages.sagemaker-mlflow
    • python313Packages.sagemaker-mlflow
    • python312Packages.sagemaker-mlflow
  • @LeSuisse accepted
  • @LeSuisse published on GitHub
MLflow Use of Default Password Authentication Bypass Vulnerability

MLflow Use of Default Password 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 basic_auth.ini file. The file contains hard-coded default credentials. An attacker can leverage this vulnerability to bypass authentication and execute arbitrary code in the context of the administrator. Was ZDI-CAN-28256.

References

Affected products

MLflow
  • ==3.4.0

Matching in nixpkgs

pkgs.mlflow-server

Open source platform for the machine learning lifecycle

Ignored packages (4)

Package maintainers

Upstream PR: https://github.com/mlflow/mlflow/pull/19260
ZDI advisory: https://www.zerodayinitiative.com/advisories/ZDI-26-111/