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CVE-2025-15379 critical CVSS 10.0

Command Injection in mlflow (CVE-2025-15379)

Summary

Command injection in mlflow (CVE-2025-15379). Successful exploitation can lead to full system takeover. Exploitable via python_env.yaml. Mitigation: upgrade to 3.8.1 or later.

Original description

The description as published by the source.

A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the _install_model_dependencies_to_env() function. When deploying a model with env_manager=LOCAL, MLflow reads dependency specifications from the model artifact's python_env.yaml file and directly interpolates them into a shell command without sanitization. This allows an attacker to supply a malicious model artifact and achieve arbitrary command execution on systems that deploy the model. The vulnerability affects versions 3.8.0 and is fixed in version 3.8.2.

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-2025-15379 has been found in mlflow.
Attackers can target a specific entry point like python_env.yaml 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 3.8.1 or later.
If unsure, ask your IT team or search "mlflow CVE-2025-15379" on the vendor's site.

CVE-2025-15379 (mlflow) — CWE-77 / CVSS v3 10.0
Attack vector: remote (network-reachable) / unauthenticated / no user interaction
Attack surface: python_env.yaml
Patched: 3.8.1 — 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-77) exists in mlflow. Attackers reach the vulnerable code path via python_env.yaml without authentication.

📍 Affected scope

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

🔥 Severity

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

🔧 How to fix

Update to 3.8.1.

🛡️ 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 3.8.1

    ステージング環境で 3.8.1 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":"3.8.1"}]}]
Bitnami mlflow
[{"type":"SEMVER","events":[{"introduced":"3.8.0"},{"fixed":"3.9.0"}]}]

References

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