CVE-2025-30387: Azure AI Document Intelligence Vulnerability

CVE-2025-30387 Overview

CVE-2025-30387 is a critical path traversal vulnerability affecting Microsoft Azure AI Document Intelligence Studio. This vulnerability stems from improper limitation of a pathname to a restricted directory (CWE-22), which allows an unauthorized attacker to elevate privileges over a network. The flaw enables attackers to bypass directory restrictions and potentially access sensitive files or execute unauthorized operations within the Azure cloud environment.

Critical Impact

Unauthorized attackers can exploit this path traversal vulnerability to elevate privileges remotely without requiring authentication, potentially gaining full control over affected Azure AI Document Intelligence Studio instances.

Affected Products

• Microsoft Azure AI Document Intelligence Studio
• Cloud-based deployments utilizing Azure AI Document Intelligence services
• Organizations leveraging Azure's document processing and intelligence capabilities

Discovery Timeline

• 2025-05-13 - CVE-2025-30387 published to NVD
• 2025-05-19 - Last updated in NVD database

Technical Details for CVE-2025-30387

Vulnerability Analysis

This path traversal vulnerability exists within the Azure AI Document Intelligence Studio's file handling mechanisms. The vulnerability occurs when the application fails to properly sanitize or validate user-supplied input that specifies file paths or directory locations. Attackers can manipulate path parameters using special character sequences such as ../ (dot-dot-slash) to navigate outside intended directory boundaries.

The attack requires no authentication and can be executed remotely over the network, making it particularly dangerous in cloud environments where the service is publicly accessible. Successful exploitation could allow attackers to read sensitive configuration files, access credentials, modify critical system files, or achieve code execution through file upload abuse.

Root Cause

The root cause of CVE-2025-30387 is insufficient input validation and sanitization of user-controlled pathname parameters within the Azure AI Document Intelligence Studio application. The application fails to implement proper path canonicalization, allowing directory traversal sequences to escape the intended restricted directory structure. This violates the principle of least privilege by permitting access to files and directories beyond those explicitly intended for user interaction.

Attack Vector

The attack vector for this vulnerability is network-based, requiring no prior authentication or user interaction. An attacker can craft malicious HTTP requests containing path traversal sequences targeting the Azure AI Document Intelligence Studio endpoints. The attack complexity is low, as standard path traversal techniques can be employed to exploit this vulnerability.

Attackers may leverage this vulnerability to:

• Access sensitive configuration files containing credentials or API keys
• Read system files to gather intelligence for further attacks
• Write malicious files to achieve persistence or code execution
• Escalate privileges within the Azure environment

The vulnerability can be exploited by including path traversal sequences such as ../../../etc/passwd or similar patterns in file path parameters. When the application processes these malicious paths without proper validation, it resolves the path outside the intended directory, granting unauthorized access. For detailed technical information, refer to the Microsoft Security Update for CVE-2025-30387.

Detection Methods for CVE-2025-30387

Indicators of Compromise

• Unusual HTTP requests containing path traversal sequences (../, ..%2f, %2e%2e/) in URL parameters or request bodies targeting Azure AI Document Intelligence Studio endpoints
• Unexpected file access patterns in Azure storage logs showing access to files outside normal application directories
• Error logs indicating failed attempts to access system files or configuration directories
• Anomalous privilege escalation events or unauthorized API calls within the Azure environment

Detection Strategies

• Implement web application firewall (WAF) rules to detect and block requests containing path traversal patterns such as ../, URL-encoded variants, and Unicode representations
• Monitor Azure Activity Logs and Azure Monitor for unusual file access patterns or privilege escalation attempts
• Deploy runtime application self-protection (RASP) solutions to detect path traversal attempts at the application layer
• Enable detailed logging for Azure AI Document Intelligence Studio and correlate with SIEM systems for anomaly detection

Monitoring Recommendations

• Configure Azure Security Center alerts for suspicious activity patterns targeting Document Intelligence Studio resources
• Implement network traffic analysis to identify requests with malicious path traversal payloads
• Set up automated alerting for any access attempts to sensitive system paths or configuration files
• Review Azure audit logs regularly for signs of exploitation attempts or successful privilege escalation

How to Mitigate CVE-2025-30387

Immediate Actions Required

• Review the Microsoft Security Update for CVE-2025-30387 and apply all recommended patches or mitigations immediately
• Enable Azure Web Application Firewall (WAF) with OWASP Core Rule Set to block common path traversal attack patterns
• Implement network segmentation to limit exposure of Azure AI Document Intelligence Studio to trusted networks only
• Audit current access controls and ensure principle of least privilege is enforced across all related Azure resources

Patch Information

Microsoft has released security guidance for this vulnerability. Organizations should consult the Microsoft Security Response Center (MSRC) advisory for specific patch information and remediation steps. As this is a cloud-based Azure service, Microsoft may apply server-side fixes automatically. However, customers should verify their deployment configuration and ensure they are following Microsoft's recommended security practices.

Workarounds

• Enable strict input validation at the application gateway level to filter path traversal sequences before they reach the application
• Configure Azure Network Security Groups (NSGs) to restrict inbound traffic to Azure AI Document Intelligence Studio from known and trusted IP ranges only
• Implement API Management policies to sanitize and validate all incoming requests containing file path parameters
• Deploy Azure DDoS Protection and WAF with custom rules specifically targeting path traversal attack signatures

# Example Azure CLI commands for enhanced network security
# Restrict access to Document Intelligence Studio via Network Security Group
az network nsg rule create \
  --resource-group <resource-group-name> \
  --nsg-name <nsg-name> \
  --name RestrictDocIntelligenceAccess \
  --priority 100 \
  --source-address-prefixes <trusted-ip-range> \
  --destination-port-ranges 443 \
  --access Allow \
  --protocol Tcp \
  --direction Inbound

# Enable Azure WAF on Application Gateway
az network application-gateway waf-config set \
  --gateway-name <app-gateway-name> \
  --resource-group <resource-group-name> \
  --enabled true \
  --firewall-mode Prevention \
  --rule-set-type OWASP \
  --rule-set-version 3.2