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CVE-2026-4035 high CVSS 7.7

Vulnerability in mlflow (CVE-2026-4035)

Summary

Vulnerability in mlflow (CVE-2026-4035). Confidential information can be exposed externally. Exploitable via api_key. Mitigation: upgrade to 3.11.0 or later.

Original description

The description as published by the source.

A vulnerability in mlflow/mlflow versions prior to 3.11.0 allows for the resolution of environment variables in AI Gateway secrets, which can be exploited to exfiltrate sensitive server-side environment credentials to an attacker-controlled endpoint. This issue arises because the api_key field in gateway secrets can accept $ENV_VAR references, which are resolved against the MLflow server's environment during runtime. The resolved secrets are then sent in provider authentication headers to the configured upstream api_base. This vulnerability can be exploited by low-privileged authenticated users in basic-auth deployments or by unauthenticated users in default deployments without basic-auth. The impact includes potential leakage of sensitive credentials such as cloud artifact credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY), which could lead to artifact poisoning and cross-boundary code execution in downstream environments. The issue is fixed in version 3.11.0.

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-2026-4035 has been found in mlflow.
Attackers can target a specific entry point like api_key over the network to misuse the product.
Confidential information can be exposed externally. CVSS score: 7.7/10.
What to do: upgrade mlflow to 3.11.0 or later.
If unsure, ask your IT team or search "mlflow CVE-2026-4035" on the vendor's site.

CVE-2026-4035 (mlflow) — CWE-201 / CVSS v3 7.7
Attack vector: remote (network-reachable) / no user interaction
Attack surface: api_key / api_base / AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY
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-201) exists in mlflow. Attackers reach the vulnerable code path via api_key.

📍 Affected scope

Check the vendor advisory for the affected versions of mlflow. Attack surface: api_key / api_base / AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY.

🔥 Severity

Severity: High (CVSS 7.7/10). Confidential information can be exposed externally.

🔧 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

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

References

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