CVE-2023-35625 in Azure Machine Learning SDK
Summary
by MITRE • 12/12/2023
Azure Machine Learning Compute Instance for SDK Users Information Disclosure Vulnerability
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Analysis
by VulDB Data Team • 05/20/2026
This vulnerability affects Azure Machine Learning Compute Instances that are configured for SDK users, representing a critical information disclosure flaw that can compromise the security of machine learning workloads in cloud environments. The vulnerability stems from improper access controls and insufficient authentication mechanisms within the compute instance's SDK interface, allowing unauthorized users to access sensitive information about the compute environment, including resource configurations, authentication tokens, and operational details that should remain restricted to authorized personnel. The flaw specifically impacts the way the system handles authentication requests and session management for SDK-based access, creating potential attack vectors that could be exploited by malicious actors with minimal privileges.
The technical implementation of this vulnerability involves the failure to properly validate user credentials and authorization levels when processing SDK requests to the compute instance. When SDK users attempt to access compute resources through the Azure Machine Learning platform, the system does not adequately verify that the requesting user has appropriate permissions to view specific operational details. This misconfiguration results in the exposure of sensitive data including but not limited to compute instance metadata, resource utilization metrics, and configuration parameters that could be leveraged for further exploitation. The vulnerability is particularly concerning as it affects the core functionality of Azure Machine Learning's security model, where proper isolation between different users and workloads is expected to be maintained.
The operational impact of this information disclosure vulnerability extends beyond simple data exposure, potentially enabling more sophisticated attacks such as privilege escalation, lateral movement, and comprehensive reconnaissance of the machine learning environment. Attackers could use the disclosed information to understand the compute instance's configuration, identify potential weaknesses in the infrastructure, and plan more targeted attacks against the broader Azure environment. The exposure of authentication tokens and session details could allow attackers to impersonate legitimate users and gain access to additional resources within the same subscription or tenant. This vulnerability directly violates the principle of least privilege and can lead to significant data breaches when combined with other exploitation techniques.
Organizations should implement immediate mitigations including enhanced access controls for compute instances, regular security assessments of SDK configurations, and monitoring for unauthorized access attempts. The recommended approach involves configuring proper role-based access controls within Azure, ensuring that only authorized users can access compute instance metadata through SDK interfaces. Additionally, implementing network segmentation and using Azure's built-in security features such as Azure Monitor and Security Center can help detect and prevent exploitation attempts. The vulnerability aligns with CWE-200, which addresses information exposure, and maps to ATT&CK technique T1566 for credential harvesting and information gathering. Regular security updates and proper configuration management are essential to prevent exploitation, while organizations should also consider implementing additional logging and alerting mechanisms to detect potential abuse of this vulnerability.