Azure Machine Learning Notebook Spoofing Vulnerability (Microsoft Azure Machine Learning)
At a glance
- Source
- NVD
- EPSS
- EPSS 0.50 % · percentile 43 ()
- State
- PUBLISHED
- Published
- Updated
- Assigner
- microsoft
- NVD status
- Analyzed
Description
Improper neutralization of special elements in output used by a downstream component ('injection') in Azure Machine Learning allows an unauthorized attacker to perform spoofing over a network.
Weaknesses (CWE)
- CWE-74 — Improper Neutralization of Special Elements in Output Used by a Downstream Component
References
Scores
| Version | Score | Vector | Scored by |
|---|---|---|---|
| CVSS 3.1 | HIGH 8.2 | CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:L/A:N/E:U/RL:O/RC:C | microsoft |
| CVSS 3.1 | HIGH 8.2 | CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:L/A:N | NVD · secure@microsoft.com (Secondary) |
European references
- ENISA EUVD · EUVD-2026-29580
- GCVE · GCVE-0-2026-33833
CERT-FR advisories
- CERTFR-2026-AVI-0587 — Multiples vulnérabilités dans Microsoft Azure · cert.ssi.gouv.fr
Affected configurations (NVD CPE)
cpe:2.3:a:microsoft:azure_machine_learning:3.0.0:*:*:*:*:*:*:*
Affected products
| Product | Versions |
|---|---|
| Microsoft · Azure Machine Learning | affected 3.0.0 < 1.7.6 |
Sources
| Source | Licence |
|---|---|
| CVE List (CVEProject cvelistV5) | CVE Program Terms of Use |
| EPSS (FIRST.org) | FIRST — attribution requested |
| ENISA EUVD | ENISA — reproduction authorised, source acknowledged |
| CERT-FR (ANSSI) | Licence Ouverte / Etalab 2.0 |
| NVD (NIST) via fkie-cad/nvd-json-data-feeds | Public domain — this product uses data from the NVD API but is not endorsed or certified by the NVD |