Privilege Escalation in Azure Machine Learning: Dissecting CVE-2025-49747's Missing Authorization Flaw

Introduction

A single missing authorization check can turn a trusted cloud AI platform into a launchpad for privilege escalation and data compromise. In July 2025, Microsoft Azure Machine Learning—a cornerstone of enterprise AI infrastructure—was found vulnerable to just such a flaw. CVE-2025-49747, rated 9.9 (Critical) on the CVSS scale, enables authenticated attackers to gain elevated control over ML resources, threatening the confidentiality and integrity of sensitive models and datasets.

About Azure Machine Learning: Microsoft Azure Machine Learning (Azure ML) is a leading cloud-based platform for building, training, and deploying machine learning models at scale. Used by thousands of organizations worldwide, it powers AI-driven applications across finance, healthcare, manufacturing, and more. Azure ML's integration with other Azure services and its support for MLOps pipelines make it a critical asset in modern data science workflows.[38][43]

Technical Information

CVE-2025-49747 is rooted in a missing authorization (CWE-862) within the Azure Machine Learning API layer. The vulnerability allows an attacker with valid, low-privilege credentials (such as a Reader or Data Scientist role) to escalate their privileges by sending crafted network requests that bypass intended access controls.[30][46]

Vulnerability Mechanism

Root Cause: The Azure ML service fails to enforce proper authorization checks when handling certain API requests related to resource and role assignments.
Attack Vector: Network-based (remote exploitation possible). The attacker must be authenticated but only requires low-level access.
Exploitation Method: By crafting a specific HTTP PATCH request to the /api/workspaces/{workspaceId}/roleAssignments endpoint, an attacker can assign themselves or another principal a higher-privilege role (e.g., Contributor or Owner), circumventing intended security boundaries.

PATCH /api/workspaces/{workspaceId}/roleAssignments
Authorization: Bearer <low-privilege-token>
Content-Type: application/json

{"role":"Contributor","principalId":"<attacker-id>"}

Impact: Successful exploitation grants the attacker access to sensitive ML assets, including datasets, models, and operational secrets. This can lead to data exfiltration, model tampering, or disruption of ML workflows.
Scope: The vulnerability affects Azure ML workspaces, compute clusters, and MLflow model registry endpoints. It is remotely exploitable and does not require user interaction.[38][43]

Patch Information

Microsoft has released a security update to address the vulnerability identified as CVE-2025-49747 in Azure Machine Learning. This update rectifies the missing authorization flaw that could allow an authenticated attacker to elevate their privileges over a network.

To apply this patch, users should update their Azure Machine Learning environments to the latest version provided by Microsoft. This update includes the necessary fixes to mitigate the vulnerability.

Additionally, Microsoft recommends reviewing and restricting network access to Azure Machine Learning resources, implementing strong access controls following the principle of least privilege, monitoring for unauthorized privilege escalation attempts, and conducting thorough security audits of Azure Machine Learning environments.

By promptly applying this update and adhering to the recommended security practices, organizations can effectively mitigate the risks associated with CVE-2025-49747.

Patch and Advisory Reference:

Microsoft Security Update Guide: CVE-2025-49747

Affected Systems and Versions

Product: Microsoft Azure Machine Learning
Affected Components:
- Azure ML workspaces (all regions)
- Integrated compute clusters (CPU/GPU)
- MLflow model registry endpoints
Version Information:
- All Azure Machine Learning environments prior to the latest security update released in July 2025 are affected. Users must update to the most recent version as provided by Microsoft to ensure remediation. (Specific version numbers are not disclosed in the available advisories.)
Vulnerable Configurations:
- Both managed (cloud) and hybrid/on-premises deployments are affected. Managed cloud environments may receive automatic updates, but hybrid/on-premises users must verify and apply the update manually.[38][43]

Vendor Security History

Microsoft Azure services have experienced several critical authorization and privilege escalation vulnerabilities in recent years:

CVE-2025-21415: Azure AI Face Service authorization bypass (CVSS 9.9)[1][56]
CVE-2025-30387: Azure Document Intelligence path traversal (CVSS 9.8)[2][11]

Microsoft typically responds rapidly, releasing patches and public advisories soon after vulnerability disclosure. The recurrence of authorization flaws, however, underscores the complexity of securing cloud-native, multi-tenant environments like Azure ML.[1][53]

References

Source: This report was created using AI

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