CVE-2025-49747 is a critical security vulnerability identified in Microsoft Azure Machine Learning, classified under CWE-862 (Missing Authorization). This vulnerability arises due to insufficient authorization checks within the Azure Machine Learning service, allowing an attacker who already has some level of authorized access to escalate their privileges over the network. The vulnerability does not require user interaction and can be exploited remotely (AV:N), with low attack complexity (AC:L). The attacker must have some privileges (PR:L) but can leverage this flaw to gain complete control over the affected system, impacting confidentiality, integrity, and availability (C:H/I:H/A:H). The scope is changed (S:C), meaning the vulnerability affects resources beyond the initially compromised component. The CVSS 3.1 base score is 9.9, indicating a critical severity level. Although no known exploits are currently reported in the wild, the potential for exploitation is significant given the nature of the vulnerability and the critical role Azure Machine Learning plays in enterprise AI workloads. Missing authorization means that certain operations or resources are accessible without proper permission checks, which can lead to unauthorized privilege escalation, data exposure, or manipulation of machine learning models and pipelines. This could undermine the trustworthiness of AI outputs and potentially disrupt business operations relying on these services.

For European organizations, the impact of CVE-2025-49747 could be severe. Azure Machine Learning is widely used across sectors such as finance, healthcare, manufacturing, and government for deploying AI models that support critical decision-making processes. Exploitation of this vulnerability could allow attackers to manipulate AI models, access sensitive training data, or disrupt AI-driven services, leading to data breaches, financial loss, reputational damage, and regulatory non-compliance (e.g., GDPR violations). Given the criticality of AI in digital transformation initiatives, the disruption or compromise of Azure Machine Learning environments could have cascading effects on business continuity and innovation efforts. Additionally, unauthorized privilege escalation could facilitate lateral movement within cloud environments, increasing the risk of broader cloud infrastructure compromise. The absence of known exploits does not diminish the urgency, as threat actors may develop exploits rapidly once the vulnerability details are public.

European organizations using Azure Machine Learning should immediately review their access controls and privilege assignments to minimize the number of users with elevated privileges. Implement strict role-based access control (RBAC) policies and enforce the principle of least privilege. Monitor Azure Machine Learning activity logs for unusual access patterns or privilege escalations. Since no patch links are currently available, organizations should engage with Microsoft support and subscribe to Azure security advisories for timely updates. Employ network segmentation and conditional access policies to restrict access to Azure Machine Learning resources from trusted networks and devices only. Consider implementing additional compensating controls such as multi-factor authentication (MFA) for all users with elevated privileges and continuous security monitoring using Azure Security Center or third-party cloud security posture management (CSPM) tools. Prepare incident response plans tailored to cloud AI service compromises, including rapid revocation of compromised credentials and forensic analysis capabilities.