Critical Microsoft Azure ML Vulnerability (CVE-2025-30390) & How to Protect Your Data

In April 2025, Microsoft disclosed a critical security vulnerability in Azure Machine Learning (Azure ML), identified as CVE-2025-30390. This flaw, stemming from improper authorization mechanisms, allows authorized attackers to escalate their privileges over a network, potentially compromising the integrity and confidentiality of machine learning workloads.
Technical Details
CVE-2025-30390 is categorized under CWE-285: Improper Authorization. The vulnerability arises from insufficient enforcement of authorization checks within Azure ML's compute environments. An attacker with low-level privileges can exploit this weakness to gain elevated access, enabling them to perform administrative actions that should be restricted. The vulnerability has been assigned a CVSS v3.1 base score of 9.9, indicating its critical severity. The CVSS vector string is:
This score reflects the following attributes:

• Attack Vector (AV): Network
• Attack Complexity (AC): Low
• Privileges Required (PR): Low
• User Interaction (UI): None
• Scope (S): Changed
• Confidentiality Impact (C): High
• Integrity Impact (I): High
• Availability Impact (A): High

This combination indicates that the vulnerability is exploitable over a network with low complexity, requires minimal privileges, and can significantly impact confidentiality, integrity, and availability.
Impact and Exploitation
The improper authorization in Azure ML allows attackers to perform actions beyond their intended permissions. This could include accessing sensitive data, modifying machine learning models, or disrupting services. As of the latest reports, there is no evidence of public proof-of-concept exploits or active exploitation in the wild. However, the high severity of the vulnerability necessitates prompt attention.
Mitigation and Response
Microsoft has addressed this vulnerability through security updates. Organizations utilizing Azure ML are advised to:

• Apply Security Updates: Ensure that all Azure ML environments are updated with the latest security patches provided by Microsoft.
• Review Access Controls: Conduct thorough audits of user permissions and implement strict role-based access control (RBAC) to limit privileges to necessary levels.
• Monitor for Anomalies: Implement continuous monitoring to detect unusual activities that may indicate exploitation attempts.

Microsoft's advisory on CVE-2025-30390 provides detailed guidance on addressing this issue.
Conclusion
CVE-2025-30390 highlights the critical importance of robust authorization mechanisms in cloud-based machine learning platforms. Organizations must remain vigilant, ensuring that security best practices are followed to protect sensitive data and maintain the integrity of their machine learning operations.

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Source: MSRC Security Update Guide - Microsoft Security Response Center