Vulnerability in mlflow (CVE-2026-69146)
Summary
Vulnerability in mlflow (CVE-2026-69146). Data can be tampered with by attackers. Exploitable via POST /api/2.0/mlflow/runs/log-inputs. Mitigation: upgrade to 3.15.0 or later.
Original description
The description as published by the source.
MLflow is an open source AI engineering platform for agents, large language models, and machine learning models. From 3.13.0 until 3.15.0, LogInputs is absent from BEFORE_REQUEST_HANDLERS in the mlflow/server/auth package, allowing any authenticated user to call POST /api/2.0/mlflow/runs/log-inputs for another user's run_id and inject attacker-controlled DatasetInput records into the dataset_inputs lineage metadata without UPDATE permission. This issue is fixed in version 3.15.0.
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-2026-69146 has been found in mlflow.
Attackers can target a specific entry point like POST /api/2.0/mlflow/runs/log-inputs over the network to misuse the product.
Data can be tampered with by attackers. CVSS score: 6.5/10.
What to do: upgrade mlflow to 3.15.0 or later.
If unsure, ask your IT team or search "mlflow CVE-2026-69146" on the vendor's site.
CVE-2026-69146 (mlflow) — CWE-862 / CVSS v3 6.5
Attack vector: remote (network-reachable) / no user interaction
Attack surface: POST /api/2.0/mlflow/runs/log-inputs / POST /api/2.0/mlflow/runs/log-metric / GET /api/2.0/mlflow/runs/get / LogInputs
Patched: 3.15.0 — 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.
A vulnerability (CWE-862) exists in mlflow. Attackers reach the vulnerable code path via POST /api/2.0/mlflow/runs/log-inputs.
Check the vendor advisory for the affected versions of mlflow. Attack surface: POST /api/2.0/mlflow/runs/log-inputs / POST /api/2.0/mlflow/runs/log-metric / GET /api/2.0/mlflow/runs/get / LogInputs.
Severity: Medium (CVSS 6.5/10). Data can be tampered with by attackers.
Update to 3.15.0.
Until the patch is applied: disable the affected feature, apply WAF rules, or restrict access via network ACLs.
Search webserver/proxy logs for unusual POST /api/2.0/mlflow/runs/log-inputs requests with malformed payloads or SQL meta-characters. 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
-
1Identify 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。 -
2Hunt for indicators of compromise detect
grep 'POST /api/2.0/mlflow/runs/log-inputs' /var/log/nginx/access.log | grep -E '(unusual_payload|sqli_pattern)'アクセスログで
POST /api/2.0/mlflow/runs/log-inputsへの異常なリクエスト (不正な認証ヘッダ・SQLメタ文字)を過去 30〜90日分捜索。WAF/SIEM があれば該当パスのアラート発火履歴を確認。 -
3Apply patch patch
Upgrade mlflow to 3.15.0ステージング環境で 3.15.0 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と、Step 2 で見つけた異常検知の続報チェックを含めること。
-
4Post-deployment verification verify
Replay attack against POST /api/2.0/mlflow/runs/log-inputs on staging to confirm patch closes the vector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 Step 2 と同じログクエリでアラート再発が無いか継続監視。
Affected packages
References
- advisory github.com
- advisory nvd.nist.gov
- advisory github.com
- package github.com
- patch github.com
- patch github.com
- web github.com
- web github.com