CVE-2025-49746
📋 TL;DR
CVE-2025-49746 is an improper authorization vulnerability in Azure Machine Learning that allows authenticated attackers to escalate privileges over the network. This affects organizations using Azure Machine Learning services where attackers with initial access can gain higher-level permissions. The vulnerability enables unauthorized access to sensitive data and resources within the ML environment.
💻 Affected Systems
- Azure Machine Learning
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
Complete compromise of Azure Machine Learning workspace, including exfiltration of training data, model theft, unauthorized code execution, and lateral movement to connected Azure resources.
Likely Case
Unauthorized access to sensitive ML datasets, model manipulation, and privilege escalation within the ML workspace leading to data breaches.
If Mitigated
Limited impact with proper network segmentation, least privilege access controls, and monitoring in place to detect anomalous privilege escalation attempts.
🎯 Exploit Status
Exploitation requires authenticated access but the privilege escalation mechanism appears straightforward once initial access is obtained.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: Latest Azure Machine Learning service version (auto-applied by Microsoft)
Vendor Advisory: https://msrc.microsoft.com/update-guide/vulnerability/CVE-2025-49746
Restart Required: No
Instructions:
1. No customer action required for patching. 2. Microsoft has deployed the fix to all affected Azure regions. 3. Ensure your Azure Machine Learning service is running the latest version by checking service health.
🔧 Temporary Workarounds
Implement Least Privilege Access
allRestrict user permissions to minimum required for their role using Azure RBAC
az role assignment create --assignee <user> --role <minimal-role> --scope <resource>
Enable Network Security Controls
allRestrict network access to Azure Machine Learning using Private Endpoints and NSGs
az network private-endpoint create --connection-name <name> --resource-group <rg> --subnet <subnet> --private-connection-resource-id <ml-workspace-id>
🧯 If You Can't Patch
- Implement strict network segmentation and limit access to Azure Machine Learning endpoints
- Enable enhanced monitoring and alerting for privilege escalation attempts and unusual access patterns
🔍 How to Verify
Check if Vulnerable:
Check Azure Service Health portal for any active incidents related to CVE-2025-49746 in your regionCheck Version:
az ml workspace show --name <workspace-name> --resource-group <rg> --query provisioningStateVerify Fix Applied:
Verify your Azure Machine Learning workspace shows no vulnerabilities in Microsoft Defender for Cloud recommendations📡 Detection & Monitoring
Log Indicators:
- Unusual privilege escalation events in Azure Activity Logs
- Multiple failed authentication attempts followed by successful privilege changes
- Unexpected role assignment changes for ML workspace
Network Indicators:
- Unusual API calls to ML authorization endpoints
- Suspicious traffic patterns to ML management interfaces
SIEM Query:
AzureActivity | where OperationNameValue contains 'Microsoft.MachineLearningServices' and (OperationNameValue contains 'write' or OperationNameValue contains 'roleAssignment') | where Caller != expected_service_principal