SSRF (Server-Side Request Forgery) in mlflow (CVE-2026-71211)
Summary
SSRF in mlflow (CVE-2026-71211). Confidential information can be exposed externally.
Original description
The description as published by the source.
MLflow's AI Gateway accepts an auth_config.api_base value when creating a gateway secret (mlflow/server/handlers.py, _create_gateway_secret) with no validation of scheme, host, or IP range; the value is stored verbatim. The gateway proxy endpoint (mlflow/server/gateway_api.py, raw_proxy) subsequently issues an HTTP request to that stored api_base plus a caller-supplied path and returns the full response body. MLflow's existing SSRF guard, _validate_webhook_url (which blocks non-global and metadata IPs), is never invoked anywhere in this gateway secret/proxy code path. The CreateGatewaySecret action additionally has no entry in the permission-validator map, so it requires only basic authentication rather than any specific scope, meaning any authenticated user — including read-only accounts — can create a secret pointing at an internal address and reach it via the proxy endpoint, potentially exposing cloud-instance IAM credentials via metadata services. This is related to CVE-2026-4035, which addresses a distinct mechanism in the same gateway-secret feature (server-side $ENV_VAR resolution inside the api_key field leaking credentials to the configured upstream); the finding here is an independent missing-validation gap in the api_base destination itself, unaffected by that fix.
Source: NIST National Vulnerability Database NVD GitHub Advisory
Auto-generated summary
Assembled by SecPulse from the source data (CVSS, CWE, affected versions) using templates — no AI involved. Check the original description and references below for exact details.
A vulnerability tracked as CVE-2026-71211 has been found in mlflow.
Confidential information can be exposed externally. CVSS score: 7.1/10.
What to do: apply the vendor's official patched release.
If unsure, ask your IT team or search "mlflow CVE-2026-71211" on the vendor's site.
CVE-2026-71211 (mlflow) — CWE-918 / CVSS v3 7.1
Attack vector: remote (network-reachable) / no user interaction
Plan: 1) Audit SBOM/dependencies, 2) Stage→prod upgrade, 3) Add WAF/proxy monitoring on affected endpoints, 4) Hunt IOCs in logs.
Refs: see the GHSA / vendor advisory / patched release linked on this page.
SSRF (CWE-918) exists in mlflow.
Check the vendor advisory for the affected versions of mlflow.
Severity: High (CVSS 7.1/10). Confidential information can be exposed externally.
Update to the patched release per vendor advisory.
Until the patch is applied: disable the affected feature, apply WAF rules, or restrict access via network ACLs.
Search webserver/proxy logs for unusual request patterns matching this CVE's known IOCs. Run grep -r 'mlflow' . against your dependency files (package-lock.json, requirements.txt, go.sum) to find affected services.
Response actions
Concrete steps and command examples for SOC/SRE teams to execute in order
-
1Identify exposure identify
grep -r 'mlflow' . | grep -v node_modulesリポジトリと本番環境の依存ファイル (package-lock.json / requirements.txt / go.sum / Gemfile.lock 等) で
mlflowを grep し、稼働しているサービス・バージョンを把握する。 -
2Post-deployment verification verify
Confirm patched version is live in productionパッチ適用後、ステージングで PoC または同等の悪用パターンを再現して脆弱性が閉じたことを確認。本番ではアクセスログ・WAF でアラート再発が無いか継続監視。
Affected packages
References
- web github.com
- web nvd.nist.gov
- web github.com
- web github.com