CVE-2025-49746: Azure Machine Learning Escalation Flaw

CVE-2025-49746 Overview

CVE-2025-49746 is an improper authorization vulnerability [CWE-285] in Microsoft Azure Machine Learning. An authenticated attacker can exploit the flaw over a network to elevate privileges within the affected service. Microsoft rates the issue at CVSS 8.8, with high impact to confidentiality, integrity, and availability.

The vulnerability requires low-privileged access but no user interaction, making it attractive for attackers who already hold a foothold in an Azure tenant. Successful exploitation can grant control over Azure Machine Learning workspaces, models, and associated data pipelines.

Critical Impact
An authenticated attacker with low privileges can escalate to higher-privileged roles within Azure Machine Learning over the network, gaining access to sensitive ML workloads, training data, and compute resources.

Affected Products

Microsoft Azure Machine Learning service
Azure ML workspaces with network-accessible endpoints
ML pipelines and compute resources tied to the affected service

Discovery Timeline

2025-07-18 - CVE CVE-2025-49746 published to NVD
2025-08-14 - Last updated in NVD database

Technical Details for CVE-2025-49746

Vulnerability Analysis

The flaw lies in how Azure Machine Learning enforces authorization on network-accessible operations. The service fails to consistently validate that the calling principal holds the privileges required for the requested action. An authorized user can issue requests that the platform should reject and instead receive higher-privileged access.

The vulnerability is mapped to CWE-285 (Improper Authorization). Exploitation does not require user interaction and operates over the network. The attacker must already possess a valid identity in the tenant, such as a contributor or guest with workspace access. From that position, the attacker can pivot to actions normally restricted to owners or service-level administrators.

The EPSS score sits at approximately 1.54%, placing this CVE in the 81st percentile of likelihood for observed exploitation activity. Microsoft has addressed the issue server-side, so no customer action is required to receive the fix.

Root Cause

The root cause is missing or incomplete authorization checks on specific Azure Machine Learning API operations. The service trusts the caller's session context without re-validating role assignments against the target resource. This gap allows lateral movement between resource scopes that should remain isolated.

Attack Vector

The attack vector is network-based and authenticated. An attacker with any valid Azure identity that has minimal access to an Azure ML workspace can craft API calls targeting privileged operations. The platform processes the request without rejecting it for insufficient role membership. Refer to the Microsoft Security Update CVE-2025-49746 for vendor details.

No public proof-of-concept exploit has been published, and the CVE is not listed in the CISA Known Exploited Vulnerabilities catalog.

Detection Methods for CVE-2025-49746

Indicators of Compromise

Unexpected role assignment changes or escalations within Azure Machine Learning workspaces
API calls to privileged Azure ML operations originating from low-privileged service principals or user accounts
Creation of new compute targets, datastores, or pipelines by identities that previously had read-only access
Anomalous access to training data, registered models, or notebook artifacts outside normal usage patterns

Detection Strategies

Review Azure Activity Log and Azure ML diagnostic logs for Microsoft.MachineLearningServices/* operations performed by unexpected principals
Correlate Entra ID sign-in logs with Azure ML control-plane actions to identify privilege mismatches
Baseline authorized administrator activity and alert on deviations such as off-hours role modifications or workspace configuration changes

Monitoring Recommendations

Enable Azure Monitor diagnostic settings on all Azure ML workspaces and forward logs to a central SIEM
Configure Microsoft Defender for Cloud alerts for suspicious Azure ML activity and unauthorized API usage
Track service principal usage and rotate credentials for identities with workspace access

How to Mitigate CVE-2025-49746

Immediate Actions Required

Confirm the Microsoft server-side fix has been applied to your Azure region by reviewing the Microsoft Security Update CVE-2025-49746 advisory
Audit all role assignments on Azure Machine Learning workspaces and remove unnecessary contributor or guest access
Rotate keys, tokens, and service principal secrets associated with affected workspaces
Review recent Azure ML activity logs for evidence of unauthorized privilege escalation

Patch Information

Microsoft addressed CVE-2025-49746 in the Azure Machine Learning service. Because Azure ML is a managed service, the patch is deployed by Microsoft and does not require customer installation. Verify regional rollout status through the Microsoft Security Response Center advisory.

Workarounds

Enforce least-privilege role assignments using Azure role-based access control (RBAC) on Azure ML workspaces
Restrict workspace access to private endpoints and disable public network access where feasible
Require Conditional Access policies and multi-factor authentication for all identities with Azure ML permissions
Enable Privileged Identity Management (PIM) for just-in-time elevation to sensitive Azure ML roles

bash

# Audit current role assignments on an Azure ML workspace
az role assignment list \
  --scope /subscriptions/<SUBSCRIPTION_ID>/resourceGroups/<RG>/providers/Microsoft.MachineLearningServices/workspaces/<WORKSPACE> \
  --output table

# Restrict workspace to private endpoint only
az ml workspace update \
  --name <WORKSPACE> \
  --resource-group <RG> \
  --public-network-access Disabled