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Azure Machine LearningApplication · Microsoft

CVE-2025-47995

HIGH · 8.8 CVSS v3.1 Published 2025-07-18
Mitigation only
No fix yet — a mitigation exists. There is no fixed release. A documented workaround reduces exposure in the meantime.
See remediation →
94/100
Remediation priority · Urgent
Remotely reachable Zero-click

Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.

NVD · unedited
Weak authentication in Azure Machine Learning allows an authorized attacker to elevate privileges over a network.

Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.

dbcve analysis · moderate confidence

This is a privilege escalation vulnerability in Azure Machine Learning stemming from weak authentication mechanisms. An attacker who already has some level of authorized access can exploit weak authentication to elevate their privileges to a higher permission level within the Azure ML service, enabling potentially unauthorized access to resources or data.

MitigationOrganizations using Azure Machine Learning should review user roles and permissions, ensure strong authentication methods are enforced (including MFA), and monitor Microsoft security advisories for any required configuration changes or patches specific to this vulnerability.

Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.

Affected products & versions What the vendor confirmedThe version ranges the vendor confirmed as vulnerable. If your version sits inside a range here, treat yourself as exposed until you have upgraded.

NVD · CPE data
Azure Machine LearningApplication
Affected:all versions

CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.

From the vector
Attack vector
Network
Complexity
Low
Privileges
Low
User interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.

dbcve checks

Work through these to decide whether this CVE applies to you.

  1. Identify Azure ML workspaces in your subscription
    Run 'az ml workspace list' or navigate to Azure Portal > Machine Learning to list all Azure ML workspaces
    Affected if Any Azure ML workspace exists in the subscription (all versions affected)
  2. Verify Azure AD authentication is enabled for Azure ML
    Check workspace configuration via Azure Portal > Machine Learning > your workspace > Authentication. Confirm Azure AD authentication is configured, not legacy or anonymous access
    Affected if Weak or legacy authentication methods (non-Azure AD) are enabled on the workspace
  3. Review role assignments for Azure ML resources
    Run 'az role assignment list --assignee <user> --scope <workspace-resource-id>' or check Access control (IAM) in the Azure Portal for each workspace. Identify users with elevated roles like Owner, Contributor, or ML Administrator
    Affected if Users have been assigned overly permissive roles beyond what they require for their job function
  4. Confirm multi-factor authentication is enforced for users with Azure ML access
    Check Microsoft Entra ID (formerly Azure AD) > Security > Authentication methods > Policy. Verify MFA is required for all users with access to Azure ML workspaces
    Affected if MFA is not required or enabled for users accessing Azure ML resources
  5. Inspect conditional access policies targeting Azure ML
    Navigate to Microsoft Entra ID > Security > Conditional Access. Review policies that apply to Azure Machine Learning or cloud apps, check if they require strong authentication
    Affected if No conditional access policies exist or policies do not enforce strong authentication (MFA, compliant device) for Azure ML access

You are affected if you have any Azure ML workspaces and weak authentication (non-Azure AD, missing MFA, or overly permissive role assignments) is configured.

Generated from the published advisory. Verify against your own configuration.

Check your environment

Paste your version and any relevant configuration and it will be compared against the affected criteria above. Do not include secrets or credentials.

AI-assisted, checked against the advisory. Informational, not a guarantee.

Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.

From vendor data
Mitigation available No clean upgrade yet — mitigate in the meantime
Mitigation

Organizations using Azure Machine Learning should review user roles and permissions, ensure strong authentication methods are enforced (including MFA), and monitor Microsoft security advisories for any required configuration changes or patches specific to this vulnerability.

Fix this in Azure Machine Learning Scoped from the published advisory
  • Consultation8.0 h
  • Implementation16.0 h
  • Testing12.0 h
  • Review / QA8.0 h
44.0 hours of engineering $7,720
Get help mitigating

An estimate, not a bill — we confirm scope with you before any work starts. Need it this week? Rush from $12,352.

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References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.

Primary sources

Practitioner notes

Contributed

Peer-ranked notes from engineers who’ve handled CVE-2025-47995 in production — separate from our analysis above.

No notes yet

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What this is

A place for practitioners to share what actually worked: a mitigation you’ve tested, a configuration change, a version- or environment-specific caveat, or a link to a verified patch. The most useful notes rise to the top as peers upvote them, so the signal stays high.

What belongs here
  • Verified mitigations, workarounds, and config changes
  • Version or environment caveats, and links to real fixes
  • No weaponised exploit code, or anything meant to cause harm
  • No spam, self-promotion, credentials, or personal data