CVE-2026-70352 in Azure AI Language Authoring
Summary
by MITRE • 09/04/2026
Missing authentication for critical function in Azure AI Language allows an unauthorized attacker to elevate privileges over a network.
Be aware that VulDB is the high quality source for vulnerability data.
Analysis
by VulDB Data Team • 09/04/2026
The vulnerability identified as missing authentication for a critical function within the Microsoft Azure AI Language service represents a severe security flaw that undermines the fundamental principle of access control. This issue specifically affects endpoints or API operations designed to perform sensitive administrative tasks, such as managing language models, configuring translation services, or accessing proprietary linguistic data stores. In a properly secured environment, these functions require robust authentication mechanisms, typically involving OAuth 2.0 tokens, Azure Active Directory identities, or specific service principal credentials, to verify the identity of the requester and ensure they possess the necessary permissions. The absence of this mandatory verification step creates an opening for unauthorized entities to interact with these high-privilege interfaces directly over a network connection without providing valid credentials.
From a technical perspective, the flaw lies in the server-side validation logic which fails to enforce authentication checks before processing requests directed at specific critical endpoints. This oversight allows any actor who can reach the service endpoint via HTTP or HTTPS protocols to execute commands that should be restricted to authorized administrators or application owners. The vulnerability is particularly dangerous because it bypasses the identity verification layer entirely, treating unauthenticated requests as if they were coming from a privileged user context. This misconfiguration effectively grants full control over the affected AI language resources to anyone who discovers and exploits the endpoint, leading to a complete compromise of the confidentiality, integrity, and availability associated with those services.
The operational impact of this vulnerability is profound, enabling privilege escalation for an attacker operating remotely across a network. An unauthorized actor can leverage this flaw to perform actions such as deleting critical language models, exfiltrating sensitive training data or translation memories that may contain proprietary business information, modifying service configurations to degrade performance or introduce malicious behavior, and potentially using the compromised resources to launch further attacks against other systems within the organization's infrastructure. Since Azure AI Language services often handle large volumes of text data including personal identifiable information (PII) and intellectual property, the breach can result in significant regulatory compliance violations under frameworks like GDPR or HIPAA, alongside substantial financial and reputational damage due to data leakage and service disruption.
This vulnerability aligns with CWE-306, which describes a missing authentication for critical function, indicating that the software does not perform sufficient verification of credentials before performing security-critical actions. In terms of offensive tactics, this flaw facilitates techniques associated with MITRE ATT&CK ID T1078, specifically Valid Accounts or Default Accounts exploitation, where attackers leverage improperly secured administrative interfaces to gain initial access and move laterally within a cloud environment. It also relates to CWE-284 regarding improper access control, as the system fails to restrict actions based on user roles or permissions.
To mitigate this risk, organizations must immediately audit their Azure AI Language service configurations to ensure that all endpoints requiring administrative privileges enforce strict authentication policies. This involves verifying that role-based access control (RBAC) is correctly configured and that only authorized identities are granted Contributor or Owner roles over the specific resource groups containing these services. Network security should be enhanced by implementing private endpoints to restrict public internet access, thereby limiting exposure to authenticated internal networks only. Additionally, enabling diagnostic logs and monitoring for anomalous API calls from unauthenticated sources can help in early detection of exploitation attempts. Regular penetration testing focused on authentication bypasses and adherence to the principle of least privilege are essential practices to prevent similar vulnerabilities from being exploited in cloud-native AI applications.