Skip to content
OpenAdvisories
Medium · 5.4 CVE-2024-3099 BIT-mlflow-2024-3099EUVD-2024-1979GHSA-8f8q-q2j7-7j2mPYSEC-2026-1645

Denial of Service and Data Model Poisoning via URL Encoding in mlflow/mlflow

Published Jun 6, 2024 · Updated Aug 1, 2024 · Assigned by @huntr_ai

Description

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. Additionally, an attacker can exploit this vulnerability to perform data model poisoning by creating a model with the same name, potentially causing an authenticated user to become a victim by using the poisoned model. The issue stems from inadequate validation of model names, allowing for the creation of models with URL-encoded names that are treated as distinct from their URL-decoded counterparts.

Affected products

VendorProductAffectedFixed
mlflow mlflow/mlflow unspecified – ≤ latest —

Affected packages

EcosystemPackageAffectedFixedRecord
PyPI mlflow < 2.11.3 2.11.3 GHSA-8f8q-q2j7-7j2m

References