Vulnerability in mlrun (CVE-2026-10766)
Summary
Vulnerability in mlrun (CVE-2026-10766). Risk of unauthorized operations or information disclosure.
Original description
The description as published by the source.
A vulnerability has been found in mlrun up to 1.12.0-rc3. This impacts the function mlrun.utils.helpers.calculate_dataframe_hash of the file mlrun/utils/helpers.py of the component DataFrame Hash Handler. The manipulation leads to use of weak hash. The attack can only be performed from a local environment. The complexity of an attack is rather high. The exploitability is said to be difficult. The exploit has been disclosed to the public and may be used. The pull request to fix this issue awaits acceptance.
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-2026-10766 has been found in mlrun.
Risk of unauthorized operations or information disclosure. CVSS score: 3.6/10.
What to do: apply the vendor's official patched release.
If unsure, ask your IT team or search "mlrun CVE-2026-10766" on the vendor's site.
CVE-2026-10766 (mlrun) — CWE-327 / CVSS v3 3.6
Attack vector: local / no user interaction
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-327) exists in mlrun.
Check the vendor advisory for the affected versions of mlrun.
Severity: Low (CVSS 3.6/10). Risk of unauthorized operations or information disclosure.
Update to the patched release per vendor advisory.
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 'mlrun' . 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 'mlrun' . | 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
mlrunを grep し、稼働しているサービス・バージョンを把握する。 -
2Post-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 vuldb.com
- advisory vuldb.com
- advisory vuldb.com
- advisory nvd.nist.gov
- advisory github.com
- advisory github.com
- package github.com
- package pypi.org
- patch github.com
- report github.com
- report vuldb.com
- web github.com