Threat actors have been exploiting a recent MLflow vulnerability to steal sensitive information, including credentials and secrets.
An open source AI engineering platform, MLflow allows users to manage the end-to-end machine learning lifecycle and deploy AI agents, LLMs, and ML models in production. It has over 27,000 GitHub stars and more than 60 million monthly downloads.
Tracked as CVE-2026-64849 (CVSS score of 9.3), the exploited security defect is described as an unauthenticated server-side request forgery (SSRF) issue that allows attackers to send HTTP requests to internal endpoints.
The issue exists because the default MLflow Tracking Server (mlflow server) exposes the model-registry webhooks API without authentication, according to MLflow’s advisory.
One of the exposed endpoints would return the upstream response status and body to the caller, and an SSRF protection that was added in version 3.10.0 could be bypassed.
