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 starting point for privilege escalation and data compromise. In July 2025, Microsoft Azure Machine Learning was found to have exactly that kind of flaw. CVE-2025-49747, rated 9.9 (Critical) on the CVSS scale, lets authenticated attackers gain elevated control over ML resources, which puts the confidentiality and integrity of sensitive models and datasets at risk.
About Azure Machine Learning: Microsoft Azure Machine Learning (Azure ML) is a cloud-based platform for building, training, and deploying machine learning models at scale. Thousands of organizations worldwide use it to run AI-driven applications in finance, healthcare, manufacturing, and other industries. Its integration with other Azure services and its support for MLOps pipelines make it a central part of modern data science workflows.[38][43]
Technical Information
CVE-2025-49747 is a missing authorization flaw (CWE-862) in the Azure Machine Learning API layer. An attacker with valid, low-privilege credentials (such as a Reader or Data Scientist role) can escalate their privileges by sending crafted network requests that bypass the intended access controls.[30][46]
Vulnerability Mechanism
- Root Cause: The Azure ML service fails to enforce proper authorization checks on certain API requests related to resource and role assignments.
- Attack Vector: Network-based, so remote exploitation is possible. The attacker must be authenticated but needs only low-level access.
- Exploitation Method: A crafted HTTP PATCH request to the
/api/workspaces/{workspaceId}/roleAssignmentsendpoint lets an attacker assign themselves or another principal a higher-privilege role, such as Contributor or Owner, and get around the 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 gives the attacker access to sensitive ML assets, including datasets, models, and operational secrets. From there, the attacker can exfiltrate data, tamper with models, or disrupt ML workflows.
- Scope: The vulnerability affects Azure ML workspaces, compute clusters, and MLflow model registry endpoints. It is remotely exploitable and requires no user interaction.[38][43]
Patch Information
Microsoft has released a security update for CVE-2025-49747 in Azure Machine Learning. It fixes the missing authorization flaw that could allow an authenticated attacker to elevate their privileges over a network.
Users should apply the patch promptly by updating their Azure Machine Learning environments to the latest version provided by Microsoft, which contains the fix.
Microsoft also recommends the following:
- Review and restrict network access to Azure Machine Learning resources.
- Implement strong access controls that follow the principle of least privilege.
- Monitor for unauthorized privilege escalation attempts.
- Run thorough security audits of Azure Machine Learning environments.
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 running a version earlier than the latest security update, released in July 2025, are affected. Users must update to the most recent version from Microsoft to remediate. The available advisories do not disclose specific version numbers.
- 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 had several critical authorization and privilege escalation vulnerabilities in recent years, including:
- 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 usually responds quickly and publishes patches and public advisories soon after a vulnerability is disclosed. Authorization flaws keep recurring, though, which shows how hard it is to secure cloud-native, multi-tenant environments like Azure ML.[1][53]
References
- NVD Entry for CVE-2025-49747
- CVE Official Entry
- Microsoft Security Update Guide: CVE-2025-49747
- Azure Machine Learning Overview
- Azure ML Data Science & Machine Learning Guide
- CWE-862: Missing Authorization
- CVE-2025-21415 Analysis
- CVE-2025-30387 Analysis
- Talos Intelligence: Microsoft Patch Tuesday May 2025
Source: This report was created using AI
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