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CVE-2026-4137 high CVSS 7.8

Vulnerability in mlflow (CVE-2026-4137)

Summary

Vulnerability in mlflow (CVE-2026-4137). Successful exploitation can lead to full system takeover. Mitigation: upgrade to 3.11.0 or later.

Original description

The description as published by the source.

In mlflow/mlflow versions prior to 3.11.0, the get_or_create_nfs_tmp_dir() function in mlflow/utils/file_utils.py creates temporary directories with world-writable permissions (0o777), and the _create_model_downloading_tmp_dir() function in mlflow/pyfunc/__init__.py creates directories with group-writable permissions (0o770). These insecure permissions allow local attackers to tamper with model artifacts, such as cloudpickle-serialized Python objects, and achieve arbitrary code execution when the tampered artifacts are deserialized via cloudpickle.load(). This vulnerability is particularly critical in environments with shared NFS mounts, such as Databricks, where NFS is enabled by default. The issue is a continuation of the vulnerability class addressed in CVE-2025-10279, which was only partially fixed.

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-2026-4137 has been found in mlflow.
Successful exploitation can lead to full system takeover. CVSS score: 7.8/10.
What to do: upgrade mlflow to 3.11.0 or later.
If unsure, ask your IT team or search "mlflow CVE-2026-4137" on the vendor's site.

CVE-2026-4137 (mlflow) — CWE-378 / CVSS v3 7.8
Attack vector: local / no user interaction
Patched: 3.11.0 — 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-378) exists in mlflow.

📍 Affected scope

Check the vendor advisory for the affected versions of mlflow.

🔥 Severity

Severity: High (CVSS 7.8/10). Successful exploitation can lead to full system takeover.

🔧 How to fix

Update to 3.11.0.

🛡️ 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 3.11.0

    ステージング環境で 3.11.0 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

pip mlflow
[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"3.11.0"}]}]
PyPI mlflow
[{"type":"ECOSYSTEM","events":[{"introduced":"1.26.0"},{"fixed":"3.11.0"}]}]
Bitnami mlflow
[{"type":"SEMVER","events":[{"introduced":"0"},{"fixed":"3.11.0"}]}]

References

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