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CVE-2025-15036 critical CVSS 10.0

Vulnerability in mlflow (CVE-2025-15036)

Summary

Vulnerability in mlflow (CVE-2025-15036). Successful exploitation can lead to full system takeover. Exploitable via extract_archive_to_dir. Mitigation: upgrade to 3.9.0rc0 or later.

Original description

The description as published by the source.

A path traversal vulnerability exists in the extract_archive_to_dir function within the mlflow/pyfunc/dbconnect_artifact_cache.py file of the mlflow/mlflow repository. This vulnerability, present in versions before v3.7.0, arises due to the lack of validation of tar member paths during extraction. An attacker with control over the tar.gz file can exploit this issue to overwrite arbitrary files or gain elevated privileges, potentially escaping the sandbox directory in multi-tenant or shared cluster environments.

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-2025-15036 has been found in mlflow.
Attackers can target a specific entry point like extract_archive_to_dir over the network to misuse the product.
Successful exploitation can lead to full system takeover. CVSS score: 10.0/10.
What to do: upgrade mlflow to 3.9.0rc0 or later.
If unsure, ask your IT team or search "mlflow CVE-2025-15036" on the vendor's site.

CVE-2025-15036 (mlflow) — CWE-29 / CVSS v3 10.0
Attack vector: remote (network-reachable) / unauthenticated
Attack surface: extract_archive_to_dir
Patched: 3.9.0rc0 — 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-29) exists in mlflow. Attackers reach the vulnerable code path via extract_archive_to_dir without authentication.

📍 Affected scope

Check the vendor advisory for the affected versions of mlflow. Attack surface: extract_archive_to_dir.

🔥 Severity

Severity: Critical (CVSS 10.0/10). Successful exploitation can lead to full system takeover.

🔧 How to fix

Update to 3.9.0rc0.

🛡️ 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 3.9.0rc0

    ステージング環境で 3.9.0rc0 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

PyPI mlflow
[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"3.9.0rc0"}]}]
Bitnami mlflow
[{"type":"SEMVER","events":[{"introduced":"0"},{"fixed":"3.9.0"}]}]

References

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