Server-side Request Forgery (SSRF) Affecting mlflow package, versions [,3.15.0)
Threat Intelligence
Snyk has reported that there have been attempts or successful attacks targeting this vulnerability.
The probability is the direct output of the EPSS model, and conveys an overall sense of the threat of exploitation in the wild. The percentile measures the EPSS probability relative to all known EPSS scores. Note: This data is updated daily, relying on the latest available EPSS model version. Check out the EPSS documentation for more details.
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Start learning- Snyk IDSNYK-PYTHON-MLFLOW-18912142
- published18 Aug 2026
- disclosed17 Aug 2026
- creditYo Iida, Ievgen Bondarenko, Katika Mohaseen, Mayur Parmar
Introduced: 17 Aug 2026
NewCVE-2026-64849 (opens in a new tab)How to fix?
Upgrade mlflow to version 3.15.0 or higher.
Overview
mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models.
Affected versions of this package are vulnerable to Server-side Request Forgery (SSRF) via a DNS rebinding attack against the webhook delivery mechanism in mlflow/webhooks/delivery.py. The original SSRF protection in _validate_webhook_url resolves the webhook hostname and checks that all resolved IPs are public, but then discards the resolved address; the subsequent requests.post call re-resolves the hostname independently. This TOCTOU gap allows an attacker to return a public IP during validation and a private or link-local IP (e.g., 169.254.169.254) at actual request time, enabling unauthenticated access to cloud metadata services or internal network resources. No authentication or user interaction is required.