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CVE-2026-2611 critical CVSS 9.6

Vulnerability in mlflow (CVE-2026-2611)

Summary

Vulnerability in mlflow (CVE-2026-2611). Successful exploitation can lead to full system takeover. Mitigation: upgrade to 3.10.0 or later.

Original description

The description as published by the source.

In MLflow version 3.9.0, the MLflow Assistant feature introduced improper origin validation in its /ajax-api endpoints. This vulnerability allows a remote attacker to exploit cross-origin requests from a malicious webpage to interact with the MLflow Assistant running on a victim's local machine. By bypassing the loopback-only restriction, the attacker can modify the Assistant's configuration to enable full access, which in turn allows the execution of arbitrary commands via the Claude Code sub-agent. This issue is resolved in version 3.10.0.

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-2026-2611 has been found in mlflow.
Successful exploitation can lead to full system takeover. CVSS score: 9.6/10.
What to do: upgrade mlflow to 3.10.0 or later.
If unsure, ask your IT team or search "mlflow CVE-2026-2611" on the vendor's site.

CVE-2026-2611 (mlflow) — CWE-346 / CVSS v3 9.6
Attack vector: remote (network-reachable) / unauthenticated
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.

❓ What is the problem

A vulnerability (CWE-346) exists in mlflow.

📍 Affected scope

Check the vendor advisory for the affected versions of mlflow.

🔥 Severity

Severity: Critical (CVSS 9.6/10). Successful exploitation can lead to full system takeover.

🔧 How to fix

Update to 3.10.0.

🛡️ Workaround

Until the patch is applied: disable the affected feature, apply WAF rules, or restrict access via network ACLs.

🔍 Detection

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

  1. 1
    Identify 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。

  2. 2
    Apply 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。

  3. 3
    Post-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

Bitnami mlflow
[{"type":"SEMVER","events":[{"introduced":"3.9.0"},{"fixed":"3.10.0"}]}]
pip mlflow
[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"3.10.0"}]}]
PyPI mlflow
[{"type":"ECOSYSTEM","events":[{"introduced":"3.9.0"},{"fixed":"3.10.0"}]}]

References

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