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Azure Machine LearningApplication · Microsoft

CVE-2025-49746

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
Improper authorization 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

Improper authorization vulnerability in Azure Machine Learning enables an authenticated attacker with low-level privileges to escalate to higher-privileged roles within the service, potentially gaining access to sensitive machine learning resources and computations.

MitigationReview and harden Azure Machine Learning role-based access control (RBAC) assignments; apply any Microsoft security updates; implement least-privilege principles for ML workspace access.

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. Confirm Azure Machine Learning workspace exists in your subscription
    Run 'az ml workspace list' to list all Azure ML workspaces, or check Azure Portal under Machine Learning resources
    Affected if No Azure ML workspaces exist - the vulnerability does not apply to environments without this service
  2. Identify all users with Azure ML role assignments
    Run 'az role assignment list --scope /subscriptions/<sub-id>/resourceGroups/<rg>/providers/Microsoft.MachineLearningServices/workspaces/<workspace>' to list role assignments, or export from Azure Portal IAM blade
    Affected if Users with baseline access (Reader, Contributor) can perform actions reserved for higher-privileged roles like Owner or custom elevated roles
  3. Review role assignments for privilege escalation patterns
    Compare each user's assigned role against the resources they accessed in Azure Activity Logs - look for users with low-privilege roles accessing high-privilege operations
    Affected if A user with Reader or Contributor role can access resources or execute operations typically restricted to Owner or Admin roles
  4. Check Azure Activity Logs for authorization failures and unexpected access
    Filter Azure Activity Logs for 'Authorization' category, look for 403 Forbidden errors or operations where the principal's role should not permit the action
    Affected if Users successfully perform actions that their assigned RBAC role should deny, or unusual patterns of elevated access from low-privileged accounts
  5. Audit custom role definitions in Azure ML
    Run 'az role definition list --scope /subscriptions/<sub-id>/resourceGroups/<rg>/providers/Microsoft.MachineLearningServices/workspaces/<workspace>' to list custom roles, review permissions for overly broad access
    Affected if Custom roles grant permissions beyond the intended scope, enabling baseline users to access higher-privilege resources or actions

A user is affected if they have Azure ML workspaces and any authenticated user with baseline access (Reader/Contributor) can perform actions or access resources reserved for higher-privileged roles.

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

Review and harden Azure Machine Learning role-based access control (RBAC) assignments; apply any Microsoft security updates; implement least-privilege principles for ML workspace access.

Fix this in Azure Machine Learning Scoped from the published advisory
  • Consultation4.0 h
  • Implementation2.0 h
  • Testing3.0 h
  • Review / QA2.0 h
11.0 hours of engineering $1,970
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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-2025-49746 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
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