Una vulnerabilidad crítica de SSRF no autenticada detectada en MLflow
MLflow has reported a critical vulnerability related to SSRF request forgery affecting versions prior to 3.15.0 of its platform. This flaw would allow an attacker to interact with the model registry's webhook API without authentication, as reported by INCIBE-CERT on August 18, 2026.
Technical details of the vulnerability
The vulnerability, identified as CVE-2026-64849, is found in the unauthenticated POST endpoint /api/2.0/mlflow/webhooks/{id}/test. In versions prior to 3.15.0, the function _validate_webhook_url() only validated the original URL, but the webhook delivery process followed redirects without restricting hostname resolution. This allowed attackers to perform SSRF requests and access internal or cloud services, obtaining information about the status and content of responses.
Implications of SSRF request forgery in MLflow
The issue lies in the fact that the validation mechanism did not block HTTP redirects or DNS rebinding attacks, making exploitation easier. By not requiring authentication, SSRF request forgery could be used to access internal resources through the vulnerable server. This poses a significant security risk for infrastructures using earlier versions of MLflow.
Recommended measures and update
To mitigate this vulnerability, MLflow has released version 3.15.0, which fixes the flaw in the validation and webhook delivery process. The update ensures that redirects and DNS resolutions are properly controlled to prevent unauthorized access. MLflow users are advised to upgrade to this version to avoid potential SSRF request forgery attacks.
Context and scope of the alert
MLflow is an open-source platform focused on artificial intelligence engineering and machine learning models. According to INCIBE, the severity of this flaw is critical due to the possibility of unauthenticated access to internal resources through SSRF techniques.
This alert highlights the need to keep development and operational tools updated in AI environments to avoid risks arising from vulnerabilities in integration and automation components.
More information and technical details can be found in the original source provided by INCIBE.
Published: 19/08/2026. Content reviewed using experience, authority and trustworthiness criteria (E-E-A-T).
This article may have used artificial intelligence tools to support structure, editing, translation or review. Editorial responsibility and final review remain with Toni Berraquero. View AI policy
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