Azure Machine Learning CVE-2025-49746: Critical Privilege Escalation via Improper Authorization

Introduction

A single misstep in cloud authorization logic can expose the crown jewels of an organization's AI strategy. In July 2025, a critical flaw in Microsoft Azure Machine Learning (Azure ML) shattered the trust boundary between authenticated users and privileged operations, enabling attackers to seize control of sensitive ML workloads and data. With a CVSS score of 9.9, CVE-2025-49746 is a wake-up call for every enterprise relying on cloud-based machine learning pipelines.

About Azure Machine Learning: Microsoft Azure is a global leader in cloud services, powering mission-critical workloads for Fortune 500 companies and startups alike. Azure Machine Learning is its flagship platform for building, deploying, and managing ML models at scale, trusted by thousands of organizations for everything from fraud detection to autonomous systems.

Technical Information

CVE-2025-49746 is a classic case of improper authorization (CWE-285) in Azure ML's access control subsystem. The vulnerability allows an authenticated attacker—someone with legitimate but limited access—to escalate their privileges over the network and perform actions reserved for higher-privilege roles.

Vulnerability Mechanism

Improper Authorization Checks: The flaw lies in how Azure ML enforces RBAC (Role-Based Access Control) boundaries. Under certain conditions, the service fails to properly validate user permissions when processing API requests or delegating tasks between linked Azure resources.
Attack Vector: Exploitation requires network access and valid credentials (user or service principal). Attackers craft API calls or manipulate resource delegation flows to bypass intended restrictions.
Impact: Successful exploitation grants SYSTEM-level access to ML compute instances, managed inference endpoints, and potentially the MLflow Model Registry. Attackers can:
- Exfiltrate proprietary model architectures
- Poison training datasets
- Inject malicious code into inference endpoints

Affected Components:

Azure ML Compute Instances (all versions prior to July 18, 2025)
Managed Inference Endpoints (especially with custom authentication modules)
MLflow Model Registry integrations with Azure Active Directory

No public proof-of-concept or detection signatures are available at this time. The technical root cause is a gap in RBAC enforcement during cross-service operations, but Microsoft has not disclosed vulnerable code snippets.

Patch Information

Microsoft has implemented backend fixes for CVE-2025-49746 across all Azure regions. Customers should:

Verify Service Version: In the Azure Portal, navigate to Machine Learning > Properties > Service Version to confirm your environment is up to date (post-July 18, 2025).
No Manual Patch Required: As Azure ML is a managed service, patching is handled by Microsoft. However, customers must validate that their resources are running the latest version and review access policies.

For further details, see the Microsoft Security Advisory and Azure ML Vulnerability Management.

Affected Systems and Versions

Azure Machine Learning Compute Instances: All versions prior to July 18, 2025
Managed Inference Endpoints: All versions prior to July 18, 2025
MLflow Model Registry (Azure AD integrations): All versions prior to July 18, 2025

No other Azure services are listed as affected in the available advisories.

Vendor Security History

Microsoft Azure has faced several critical vulnerabilities in 2025, particularly in ML and AI services:

CVE-2025-30390: Privilege escalation in Azure ML Compute (April 2025)
CVE-2025-21415: Authentication bypass in Azure AI Face Service
CVE-2025-32711: Zero-click data exfiltration in Copilot AI

Microsoft's average patch latency for cloud services is under 48 hours, and security advisories are detailed and transparent. However, recurring issues in RBAC and cross-service delegation highlight the ongoing complexity of securing cloud-native ML environments.

References

Source: This report was created using AI

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