dbcve.org
Azure Machine LearningApplication · Microsoft

CVE-2025-49747

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
Missing 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

Azure Machine Learning has a missing authorization check that allows an already-authorized attacker to elevate their privileges within the service. The vulnerability can be exploited remotely over the network, enabling a user with some access to gain higher-level permissions than intended.

MitigationReview Azure ML role assignments and implement least-privilege access controls. Monitor for unusual privilege escalation activity and apply any Microsoft-recommended Azure ML security configurations or patches.

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 check Azure Portal under Machine Learning resources to list all Azure ML workspaces deployed in your subscription
    Affected if Any Azure ML workspace exists - the vulnerability affects all versions of Azure ML
  2. Review Azure ML role assignments
    Run 'az role assignment list --scope /subscriptions/<sub-id>/resourceGroups/<rg-name>/providers/Microsoft.MachineLearningServices/workspaces/<workspace-name>' or navigate to Access control (IAM) in the Azure portal for each ML workspace to list all role assignments
    Affected if Multiple users or service principals have roles assigned on the ML workspace - the vulnerability allows privilege escalation from any assigned role
  3. Check for users with Contributor or Owner roles on ML workspaces
    In Azure Portal, go to each ML workspace > Access control (IAM) > Role assignments, or use 'az role assignment list --role Contributor/Owner --scope <workspace-scope>' to identify highly privileged users
    Affected if Users other than subscription Owners have Contributor or Owner roles on ML workspaces - these roles could be leveraged for privilege escalation
  4. Audit recent role assignment changes in Azure activity logs
    In Azure Portal, go to Monitor > Activity log and filter for 'Role assignment' operations on Microsoft.MachineLearningServices resources, or query with 'az monitor activity-log list --resource-group <rg> --operation-name "Microsoft.Authorization/roleAssignments/write"'
    Affected if Recent role assignment changes exist that may indicate exploitation attempts or unauthorized privilege escalation
  5. Review Azure ML compute identity and RBAC settings
    Navigate to each ML workspace > Compute in Azure Portal, or use 'az ml compute show' commands to check compute instances and their associated managed identities and permissions
    Affected if Compute resources have managed identities with broad permissions that could be exploited for privilege escalation after the initial attack

Your environment is affected if you have any Azure ML workspace deployed, since the vulnerability affects all versions and allows privilege escalation from any authorized user role.

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 Azure ML role assignments and implement least-privilege access controls. Monitor for unusual privilege escalation activity and apply any Microsoft-recommended Azure ML security configurations or patches.

Fix this in Azure Machine Learning Scoped from the published advisory
  • Consultation8.0 h
  • Implementation12.0 h
  • Testing6.0 h
  • Review / QA4.0 h
30.0 hours of engineering $5,380
Get help mitigating

An estimate, not a bill — we confirm scope with you before any work starts. Need it this week? Rush from $8,608.

Scan for this in your stack

Free · runs locally
dbcve dependency scanner

Check whether your project pulls in CVE-2025-49747 — or any other known-vulnerable package — straight from your lock files. Free and open source; it runs locally and uploads nothing.

Get it on GitHub Scan in the browser

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-2025-49747 in production — separate from our analysis above.

No notes yet

Be the first to add a field note for this CVE — a mitigation you’ve verified, a version caveat, or a link to a working fix. Sign in above to contribute.

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