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Vulnerabilities

1 CVEs · NVD, GitHub Advisories, CISA KEV, FIRST EPSS, GitHub PoC repos

CVEVulnerabilityCVSSEPSSFlagsAffectedExposurePublished
CVE-2026-64849
Unauthenticated SSRF in MLflow Exposes Cloud Credentials and Secrets

MLflow versions prior to 3.15.0 contain a server-side request forgery flaw (CWE-918) in the unauthenticated POST /api/2.0/mlflow/webhooks/{id}/test endpoint. The webhook URL is validated only for the original request, but delivery follows redirects and re-resolves the hostname without pinning the validated address, letting an attacker redirect server-side requests to internal network services or cloud instance metadata endpoints. Because the endpoint returns response_status and response_body, attackers can read internal service responses and, per public reporting, steal cloud credentials and secrets from metadata services. Any self-hosted MLflow deployment is affected, with internet-exposed servers at the highest risk. Exploitation is confirmed in the wild: CISA added the flaw to its Known Exploited Vulnerabilities catalog on 2026-08-19, and headlines report active attacks stealing cloud credentials; EPSS puts 30-day exploitation probability at 16.4% (97th percentile).

Do: Upgrade to MLflow 3.15.0 or later immediately, prioritizing any MLflow server reachable from the internet. Until patched, restrict network access to MLflow (especially the /api/2.0/mlflow/webhooks/{id}/test endpoint), place it behind authentication or a reverse proxy, and review webhook logs for unexpected test requests and cloud metadata endpoint access; rotate cloud credentials and secrets if compromise indicators are found. Federal agencies must apply vendor mitigations in accordance with CISA BOD 26-04 guidance or discontinue use if mitigations are unavailable.

9.316% KEV PoC ×3
  • lfprojects MLflow All versions prior to 3.15.0 (fixed in 3.15.0)
moderatelikely thousands of internet-exposed MLflow servers, from a substantially larger installed base (estimate)