CVE-2025-49746 is a critical vulnerability classified under CWE-285 (Improper Authorization) affecting Microsoft Azure Machine Learning services. This vulnerability allows an attacker who already has some level of authorized access (privilege level: PR:L) to escalate their privileges over the network without requiring user interaction (UI:N). The vulnerability impacts the confidentiality, integrity, and availability of the Azure Machine Learning environment, as indicated by the CVSS vector which scores high for all three aspects (C:H/I:H/A:H). The scope is changed (S:C), meaning the vulnerability affects resources beyond the initially authorized scope, potentially allowing attackers to access or control resources they should not have access to. The attack vector is network-based (AV:N) with low attack complexity (AC:L), making exploitation feasible in real-world scenarios once an attacker has some authorized access. The vulnerability was reserved in June 2025 and published in July 2025, with no known exploits in the wild yet and no patch links provided at the time of this report. The lack of patches suggests that organizations using Azure Machine Learning must be vigilant and implement compensating controls until a fix is released. Improper authorization in a cloud-based machine learning platform can lead to unauthorized data access, manipulation of machine learning models, theft of intellectual property, and disruption of AI-driven services, which can have severe operational and reputational consequences.

For European organizations, the impact of this vulnerability is significant due to the increasing adoption of Azure Machine Learning for AI and data analytics workloads. Unauthorized privilege escalation could lead to exposure of sensitive data, including personal data protected under GDPR, resulting in compliance violations and heavy fines. Attackers could manipulate or sabotage machine learning models, leading to incorrect business decisions, fraud, or compromised automated processes. The availability impact could disrupt critical AI services used in sectors such as finance, healthcare, manufacturing, and public services, causing operational downtime and financial losses. Given the criticality and potential for widespread impact, European organizations relying on Azure Machine Learning must prioritize risk assessment and mitigation to protect their AI infrastructure and data assets.

1. Implement strict access controls and least privilege principles for all users and service accounts interacting with Azure Machine Learning. 2. Monitor and audit all privilege escalations and access attempts within Azure Machine Learning environments using Azure Security Center and Azure Monitor. 3. Use conditional access policies and multi-factor authentication (MFA) to reduce the risk of compromised credentials being used to exploit this vulnerability. 4. Segment Azure Machine Learning workloads and data to limit the blast radius in case of exploitation. 5. Regularly review and update role-based access control (RBAC) assignments to ensure no excessive permissions are granted. 6. Stay informed about Microsoft’s security advisories and apply patches or updates immediately once available. 7. Employ network security controls such as virtual network service endpoints and private links to restrict access to Azure Machine Learning resources. 8. Conduct penetration testing and vulnerability assessments focused on authorization mechanisms within Azure environments to detect potential weaknesses proactively.