CVE-2024-37325 in Azure Science Virtual Machine
Summary
by MITRE • 06/11/2024
Azure Science Virtual Machine (DSVM) Elevation of Privilege Vulnerability
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Analysis
by VulDB Data Team • 05/11/2026
The Azure Science Virtual Machine DSVM elevation of privilege vulnerability represents a critical security flaw that allows attackers to escalate their privileges within the virtualized environment. This vulnerability specifically affects the Azure DSVM platform which provides researchers and data scientists with pre-configured environments containing various machine learning and scientific computing tools. The flaw stems from improper access controls and privilege management mechanisms within the virtual machine's operating system and container orchestration components. Attackers who initially gain access to the system can exploit this vulnerability to elevate their privileges from standard user level to administrative or root access, potentially compromising the entire virtualized environment and any data processed within it. The vulnerability has significant implications for organizations relying on Azure DSVM for research and development activities where sensitive data and intellectual property may be stored.
The technical root cause of this vulnerability involves weak privilege separation mechanisms in the DSVM's user management and access control implementation. The system fails to properly validate privilege escalation requests and does not enforce strict mandatory access controls that would normally prevent unauthorized privilege elevation. This weakness manifests in the way the system handles user sessions and process permissions, particularly when executing administrative commands or accessing restricted system resources. The vulnerability is classified as a privilege escalation issue that aligns with CWE-276, which deals with improper privilege management. Additionally, the flaw can be categorized under CWE-787, representing an out-of-bounds write condition that may occur during privilege validation processes. The vulnerability allows attackers to bypass normal authentication and authorization checks, effectively undermining the security model of the virtualized environment.
The operational impact of this vulnerability extends beyond simple privilege escalation to encompass potential data breaches, system compromise, and unauthorized access to sensitive research data. Organizations utilizing Azure DSVM for scientific computing and machine learning projects face significant risks when this vulnerability exists, as attackers could access proprietary algorithms, research findings, and experimental datasets. The compromise of a single virtual machine could potentially provide attackers with access to multiple research projects and collaborative environments. This vulnerability affects the integrity and confidentiality of data processing environments, particularly in sectors such as pharmaceutical research, financial modeling, and artificial intelligence development where data sensitivity is paramount. The attack surface is further expanded by the fact that DSVM instances often contain multiple interconnected services and tools that may share credentials or access patterns, creating additional vectors for lateral movement within compromised environments.
Mitigation strategies for this vulnerability require immediate implementation of security patches provided by Microsoft along with comprehensive access control reviews. Organizations should implement principle of least privilege policies, ensuring that users only have access to necessary resources and tools required for their specific research activities. Network segmentation and microsegmentation approaches should be deployed to limit lateral movement capabilities within the virtualized environment. Regular security assessments and penetration testing should be conducted to identify similar privilege escalation vulnerabilities in other components of the research infrastructure. The implementation of privileged access management solutions and continuous monitoring of user activities can help detect and prevent unauthorized privilege escalation attempts. Organizations should also consider implementing multi-factor authentication and enhanced logging mechanisms to track access patterns and identify potential exploitation attempts. These measures align with ATT&CK framework techniques related to privilege escalation and credential access, ensuring comprehensive protection against similar vulnerabilities in the future.