CVE-2025-49747
Published: 18 July 2025
Summary
CVE-2025-49747 is a critical-severity Missing Authorization (CWE-862) vulnerability in Microsoft Azure Machine Learning. Its CVSS base score is 9.9 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Privilege Escalation (T1068); ranked in the top 21.9% 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 Other ATLAS/OWASP Terms risk domain.
The strongest mitigations our analysis identified are NIST 800-53 AC-3 (Access Enforcement) and AC-6 (Least Privilege).
Deeper analysis
CVE-2025-49747 is a missing-authorization vulnerability (CWE-862) in Azure Machine Learning that carries a CVSS 3.1 base score of 9.9. The flaw resides in the service’s authorization logic and permits an authenticated user to perform actions beyond their assigned privileges when communicating over the network.
An attacker who already possesses a low-privileged account can exploit the issue remotely without user interaction. Successful exploitation grants the attacker full control over affected machine-learning resources, resulting in complete loss of confidentiality, integrity, and availability with a scope change that can impact other components within the Azure tenant.
Microsoft’s Security Response Center advisory at https://msrc.microsoft.com/update-guide/vulnerability/CVE-2025-49747 supplies the official guidance on required updates and configuration changes. The associated EPSS score has remained low, with a current value of 0.0107 and a peak of only 0.0114, indicating no material increase in observed exploitation interest since disclosure.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-21901
Vulnerability details
Missing authorization 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
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: machine learning
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Missing authorization in Azure Machine Learning enables an authorized attacker to perform privilege escalation over a network via exploitation.
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Requires enforcement of approved authorizations for access to system resources in Azure Machine Learning, directly addressing the missing authorization that enables privilege escalation.
Enforces least privilege to limit the scope and impact of privilege escalation from low-privilege accounts exploiting the missing authorization.
Mandates identification, reporting, and correction of flaws like this missing authorization vulnerability through timely patching as per Microsoft's advisory.