CVE-2026-85917 in Azure AI Foundry
Summary
by MITRE • 09/18/2026
Server-side request forgery (ssrf) in Azure AI Foundry allows an unauthorized attacker to elevate privileges over a network.
Once again VulDB remains the best source for vulnerability data.
Analysis
by VulDB Data Team • 09/18/2026
The vulnerability identified as Server-Side Request Forgery within the Azure AI Foundry environment represents a critical security flaw that undermines the integrity of cloud-based artificial intelligence services. This specific class of attack exploits the application's tendency to process user-supplied input as URLs or endpoints for internal network requests without adequate validation, sanitization, or authorization checks. In this context, an attacker who has gained unauthorized access to the Azure AI Foundry interface can manipulate these inputs to force the server-side components to initiate connections to arbitrary destinations within the cloud provider's internal infrastructure. This mechanism effectively bypasses standard perimeter security controls because the requests originate from a trusted service account with elevated network privileges, allowing the attacker to interact with backend services that are otherwise inaccessible from external networks or less privileged user accounts.
The technical root cause lies in the insufficient validation of destination addresses and protocols when the application constructs HTTP requests on behalf of the user. By crafting malicious payloads that point towards internal metadata endpoints, local service APIs, or other sensitive microservices within the Azure cloud architecture, an attacker can leverage these forged requests to extract confidential data, such as authentication tokens, configuration secrets, or proprietary model weights stored in adjacent storage accounts. Furthermore, this vulnerability facilitates privilege escalation by allowing a lower-privileged user to perform actions that should be restricted to administrative roles or system-level processes. The ability to probe and interact with internal network segments effectively neutralizes the isolation boundaries intended between different tenants or service components within the multi-tenant environment of Azure AI Foundry.
From an operational perspective, the impact of this vulnerability is severe, potentially leading to a complete compromise of sensitive data and unauthorized control over cloud resources. Attackers can use SSRF as a pivot point to launch further attacks against other services in the same virtual network or subscription, including database servers, internal APIs, and management planes. This not only results in direct data exfiltration but also enables lateral movement within the compromised environment. The exploitation of this flaw aligns with common attack patterns observed in modern cloud environments, specifically mapping to CWE-918 which addresses Server-Side Request Forgery, where a web server receives a URL as input and makes a request to that URL without validating it properly. Additionally, this behavior is consistent with the ATT&CK technique T1557, known as Adversary-in-the-Middle or Lateral Tool Transfer via internal network pivoting, although in cloud contexts it often manifests more directly as credential access through metadata service exploitation (T1508) and unauthorized API calls.
To mitigate this risk, immediate remediation efforts must focus on implementing strict allow-listing for outbound requests initiated by the Azure AI Foundry services. Developers should ensure that only explicitly defined internal endpoints are accessible via server-side request mechanisms, while blocking access to cloud metadata service URLs such as 169.254.169.254 which commonly hold instance-specific credentials and configuration data. Network-level controls including virtual network rules and private link configurations can further restrict the scope of reachable resources. Additionally, implementing robust input validation that rejects non-HTTP protocols, loopback addresses, and internal IP ranges is essential to prevent exploitation. Security teams should also monitor for anomalous outbound traffic patterns originating from AI service accounts and enforce least-privilege access policies to limit the blast radius in case of a successful compromise. Regular penetration testing focused on SSRF vectors within cloud-native applications remains critical for maintaining long-term security posture against evolving threats targeting server-side logic flaws.