Security readout for executives and security teams
Plain-English summary
A permissions flaw in Azure Machine Learning compute could let an already authorized, low-privilege attacker gain broader privileges remotely. Successful exploitation could expose or alter sensitive information and disrupt services. The supplied sources do not identify affected versions or required configurations, so organizations using Azure Machine Learning should assess exposure promptly.
Executive priority
Treat as an immediate assessment priority because compromise could cross a security boundary and severely affect data and operations. Establish whether Azure Machine Learning is used, confirm remediation status with Microsoft, and review privileged activity. Urgency is high despite no supplied evidence of active exploitation.
Technical view
CVE-2025-30390 is an improper-authorization vulnerability (CWE-285) in Azure Machine Learning. CVSS 3.1 rates it 9.9: network-accessible, low complexity, low privileges required, no user interaction, changed scope, and high confidentiality, integrity, and availability impact. Specific vulnerable versions and configuration prerequisites are not provided.
Likely exposure
Potential exposure exists where Azure Machine Learning compute is deployed and accessible to authorized low-privilege identities. The source bundle lists no affected version range, CPE, tenant configuration, compute type, or access prerequisite beyond authorization, preventing precise exposure determination.
Exploitation context
The attacker must already be authorized but can act over a network without user interaction. The CVSS exploit-maturity value is unproven, and the CVE is not listed as KEV in the supplied bundle. There is therefore no source-supported evidence here of active exploitation or a public exploit.
Researcher notes
The available record supports an authorization-boundary failure but does not disclose the vulnerable component, affected versions, configurations, root cause, attack path, or remediation mechanics. Avoid assuming every Azure ML deployment is vulnerable. Validation should focus on authoritative Microsoft guidance, identity reachability, authorization boundaries, and audit evidence.
Mitigation direction
- Review the Microsoft advisory for the current remediation or service-side update status.
- Inventory Azure Machine Learning workspaces, compute resources, and identities with access.
- Reduce unnecessary Azure ML permissions using least-privilege role assignments.
- Restrict network access to Azure ML resources where operationally feasible.
- Escalate remediation through Microsoft support if the advisory leaves tenant action unclear.
Validation and detection
- Confirm all Azure Machine Learning workspaces and compute resources are included in the asset inventory.
- Compare deployed configurations and service status against Microsoft's CVE advisory.
- Review role assignments for low-privilege identities with Azure ML compute access.
- Examine relevant audit logs for unexpected privilege, role, compute, or configuration changes.
- Document Microsoft's confirmation of remediation or whether the service received a platform-side update.
Public sources used
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
Conservative CVE-to-ATT&CK context
These mappings and lookup hints may be relevant to the vulnerability behavior, CWE, affected product, or exposure path. Glexia-inferred context is not an official MITRE, ATT&CK, CWE, or CVE Program mapping.
ATT&CK lookup starting points
Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.
CWE-285: Exact CWE lookup
Use the exact CWE identifier as the starting point before reviewing related ATT&CK behavior. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.
Open ATT&CK lookupPrivilege behavior lookup
The CVE wording references privilege impact, so privilege escalation and authorization behavior review may help. This is a Glexia inferred lookup path, not an official MITRE, ATT&CK, or CVE Program mapping.
Open ATT&CK lookupCVE-2025-30390 mapping review
Open the CVE-to-ATT&CK bridge for reviewed, inferred, or future official mappings tied to this CVE.
Open ATT&CK lookup- Severity
- Critical
- CVSS
- 9.9 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H/E:U/RL:O/RC:C
CNA and ADP enrichment extracted from CVE v5
These fields come from the CVE record and ADP containers, not from Glexia's Take. They preserve time-varying source decisions such as CISA SSVC, KEV status, CVSS metrics, and provider references.
SSVC decision data
CVSS vector scores
1 official scoreWe collect every scored CVSS vector available in the official CNA and ADP containers. When more than one version is present, the table keeps the source vectors side by side instead of collapsing them into the highest score.
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H/E:U/RL:O/RC:C3.16microsoftVulnerability scoring details
Base CVSS 3.1 score
9.9CriticalVector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H/E:U/RL:O/RC:C
Vulnerability timeline
Timeline events are normalized from CVE metadata, CNA source timelines, ADP timelines, and KEV metadata when present.
- CVE reservedCVE Program
The CVE ID was reserved by the assigning CNA.
- CVE publishedCVE Program
The CVE record was published.
- CVE updatedCVE Program
The CVE record metadata indicates this as the latest update time.
ADP provider summaries
Source materials
- CVE List V5 sourceCVE List V5
- Azure ML Compute Elevation of Privilege VulnerabilityCVE reference, microsoft · vendor-advisory, patch
Products and packages named in the record
CWE details
CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.
Improper Authorization
Improper Authorization represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.