CVE-2025-47995
Published: 18 July 2025
Summary
CVE-2025-47995 is a medium-severity Weak Authentication (CWE-1390) vulnerability in Microsoft Azure Machine Learning. Its CVSS base score is 6.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Privilege Escalation (T1068); ranked in the top 14.5% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Other Platforms; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified are NIST 800-53 AC-6 (Least Privilege) and IA-2 (Identification and Authentication (Organizational Users)).
Deeper analysis
CVE-2025-47995 is a weak authentication vulnerability, tracked under CWE-1390, that affects Azure Machine Learning. It carries a CVSS 3.1 base score of 6.5 reflecting network attack vector, low attack complexity, and low privileges required, with the result that an authenticated user can obtain unauthorized access to sensitive information.
An authorized attacker can exploit the flaw over a network to elevate privileges and achieve high confidentiality impact while leaving integrity and availability unaffected.
Microsoft has published an advisory for the issue at https://msrc.microsoft.com/update-guide/vulnerability/CVE-2025-47995 that includes mitigation guidance.
The associated EPSS score has remained low, with a current value of 0.0243 and a peak of 0.0258; because the component is part of Azure Machine Learning the finding is directly relevant to AI/ML deployments.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-21914
Vulnerability details
Weak authentication in Azure Machine Learning allows an authorized attacker to elevate privileges over a network.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other Platforms
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: machine learning
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Weak authentication in Azure Machine Learning enables an authorized attacker to elevate privileges over a network, directly facilitating T1068: Exploitation for Privilege Escalation.
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Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Directly requires identification and authentication of users before granting access, mitigating the weak authentication flaw that enables privilege escalation.
Enforces least privilege so that even an authenticated low-privileged user cannot escalate rights within Azure Machine Learning.
Ensures the system enforces access-control decisions based on authenticated identity, blocking unauthorized elevation after weak authentication succeeds.