SSRF (Server-Side Request Forgery) in mlflow (CVE-2026-2393)
Summary
SSRF in mlflow (CVE-2026-2393). Confidential information can be exposed externally. Exploitable via url. Mitigation: upgrade to 3.9.0 or later.
Original description
The description as published by the source.
A Server-Side Request Forgery (SSRF) vulnerability exists in MLflow versions prior to 3.9.0. The _create_webhook() function in mlflow/server/handlers.py accepts a user-controlled url parameter without validation, and the _send_webhook_request() function in mlflow/webhooks/delivery.py sends HTTP POST requests to this attacker-controlled URL. This allows an authenticated attacker to force the MLflow backend to send HTTP requests to internal services, cloud metadata endpoints, or arbitrary external servers. The lack of input sanitization, URL scheme filtering, or allowlist validation on the webhook URL enables exploitation, potentially leading to cloud credential theft, internal network access, and data exfiltration.
Source: GitHub Security Advisories (Global) 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-2026-2393 has been found in mlflow.
Attackers can target a specific entry point like url over the network to misuse the product.
Confidential information can be exposed externally. CVSS score: 7.1/10.
What to do: upgrade mlflow to 3.9.0 or later.
If unsure, ask your IT team or search "mlflow CVE-2026-2393" on the vendor's site.
CVE-2026-2393 (mlflow) — CWE-918 / CVSS v3 7.1
Attack vector: remote (network-reachable) / no user interaction
Attack surface: url
Patched: 3.9.0 — apply immediately
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. Attackers reach the vulnerable code path via url.
Check the vendor advisory for the affected versions of mlflow. Attack surface: url.
Severity: High (CVSS 7.1/10). Confidential information can be exposed externally.
Update to 3.9.0.
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
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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。 -
2Apply patch patch
Upgrade mlflow to 3.9.0ステージング環境で 3.9.0 に上げて回帰テスト → 本番反映。回帰テストはアプリの主要ハッピーパスを含めること。
-
3Post-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
- advisory huntr.com
- package github.com
- patch github.com
- web nvd.nist.gov
- web github.com