Vulnerability in mlflow (CVE-2026-2652)
Summary
Vulnerability in mlflow (CVE-2026-2652). Data can be tampered with by attackers. Mitigation: upgrade to 3.10.0 or later.
Original description
The description as published by the source.
A vulnerability in mlflow/mlflow versions 3.9.0 and earlier allows unauthenticated access to certain FastAPI routes when the server is started with authentication enabled (--app-name basic-auth) and served via uvicorn (ASGI). The FastAPI permission middleware only enforces authentication on /gateway/ routes, leaving other routes such as the Job API (/ajax-api/3.0/jobs/*) and the OpenTelemetry trace ingestion API (/v1/traces) unprotected. This allows unauthenticated remote attackers to submit jobs, read job results, cancel running jobs, and inject arbitrary trace data into experiments. The issue arises from an architectural mismatch between Flask and FastAPI authentication mechanisms, where the _find_fastapi_validator() function fails to handle non-/gateway/ paths, resulting in a complete authentication bypass. This vulnerability is fixed in version 3.10.0.
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-2026-2652 has been found in mlflow.
Data can be tampered with by attackers. CVSS score: 8.6/10.
What to do: upgrade mlflow to 3.10.0 or later.
If unsure, ask your IT team or search "mlflow CVE-2026-2652" on the vendor's site.
CVE-2026-2652 (mlflow) — CWE-305 / CVSS v3 8.6
Attack vector: remote (network-reachable) / unauthenticated / no user interaction
Patched: 3.10.0 — 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.
A vulnerability (CWE-305) exists in mlflow.
Check the vendor advisory for the affected versions of mlflow.
Severity: High (CVSS 8.6/10). Data can be tampered with by attackers.
Update to 3.10.0.
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 3.10.0ステージング環境で 3.10.0 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 nvd.nist.gov
- package github.com
- web github.com
- web huntr.com
- web github.com