OS Command Injection in mlflow (CVE-2026-0596)
Summary
OS command injection in mlflow (CVE-2026-0596). Successful exploitation can lead to full system takeover. Exploitable via model_uri. Mitigation: upgrade to 3.9.0 or later.
Original description
The description as published by the source.
A command injection vulnerability exists in mlflow/mlflow when serving a model with enable_mlserver=True. The model_uri is embedded directly into a shell command executed via bash -c without proper sanitization. If the model_uri contains shell metacharacters, such as $() or backticks, it allows for command substitution and execution of attacker-controlled commands. This vulnerability affects the latest version of mlflow/mlflow and can lead to privilege escalation if a higher-privileged service serves models from a directory writable by lower-privileged users.
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-2026-0596 has been found in mlflow.
Attackers can target a specific entry point like model_uri over the network to misuse the product.
Successful exploitation can lead to full system takeover. CVSS score: 7.8/10.
What to do: upgrade mlflow to 3.9.0 or later.
If unsure, ask your IT team or search "mlflow CVE-2026-0596" on the vendor's site.
CVE-2026-0596 (mlflow) — CWE-78 / CVSS v3 7.8
Attack vector: adjacent-network / unauthenticated / no user interaction
Attack surface: model_uri
Patched: 3.9.0 — 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.
OS command injection (CWE-78) exists in mlflow. Attackers reach the vulnerable code path via model_uri without authentication.
Check the vendor advisory for the affected versions of mlflow. Attack surface: model_uri.
Severity: High (CVSS 7.8/10). Successful exploitation can lead to full system takeover.
Update to 3.9.0.
Until the patch is applied: disable the affected feature, apply WAF rules, or restrict access via network ACLs.
Search webserver/proxy logs for unusual 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
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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。 -
2Apply patch patch
Upgrade mlflow to 3.9.0ステージング環境で 3.9.0 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。
-
3Post-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
References
- advisory huntr.com
- advisory github.com
- package github.com
- package pypi.org
- web nvd.nist.gov
- web github.com
- web github.com