Server-side request forgery enabling privilege elevation in Microsoft Azure AI Foundry

Server-side request forgery (ssrf) in Azure AI Foundry allows an unauthorized attacker to elevate privileges over a network.

The record identifies Microsoft Azure AI Foundry as affected.

• The recorded version is -, which does not provide a usable version range for precise scoping.
• No release branch or exact fixed-version boundary is supplied.
• The CVE data describes the product as an exclusively hosted service, so remediation may be performed by the service provider rather than through a customer-installed package. Customer-specific applicability details are not established.

Microsoft Azure AI Foundry contains a Server-Side Request Forgery (SSRF) weakness classified as CWE-918. The flaw allows an unauthorized attacker to influence a server-side component into making network requests in a way that benefits the attacker. The record confirms privilege elevation over a network, but does not identify the endpoint, input field, destination URL, reachable internal resources, or implementation detail that causes the issue. The supplied assessment indicates potentially serious confidentiality impact, while integrity and availability impact are not established. These limits describe the available evidence and do not establish that every SSRF path or destination resource is reachable.

The weakness is described as network reachable and exploitable by an unauthorized attacker. The supplied assessment indicates that no prior privileges and no user interaction are required, with low attack complexity. The record's public_exploit value is false, so no public exploit is identified in the supplied data. Actual exploitation status is not established because known_exploited is null; this should not be interpreted as proof that exploitation has never occurred.

The described technical outcome is privilege elevation over a network through an SSRF weakness in Azure AI Foundry. The available assessment indicates that an attacker does not need prior authentication or user interaction, and that the most significant established impact is to confidentiality. The record does not establish integrity or availability impact, does not identify the affected tenant boundary, and does not name a specific target resource. As a result, the exact data or permissions reachable in an individual environment remain unknown.

The vulnerability may permit unauthorized access to data available to the affected server-side component and may enable privilege elevation within the scope described by the record. Potential consequences include disclosure of sensitive information or expanded access to service resources. The record does not confirm a data breach, compromise of a particular tenant, data modification, or service outage. The actual consequence for an organization depends on the service configuration and resources reachable by the vulnerable component.

1. Apply the update or remediation provided by Microsoft for Azure AI Foundry. The record marks a patch as available.
2. Verify in the relevant tenant or subscription that the service-side update has been applied rather than assuming that the service is safe because no local package is installed.
3. Because the available evidence provides no exact fixed release or branch boundary, do not infer that an unverified version or parallel branch is remediated.
4. If update status cannot be verified, restrict unnecessary configurations or workflows that could cause the service to make outbound requests, where such controls are appropriate for the organization's architecture.
5. Follow subsequent Microsoft guidance to confirm service scope, required customer actions, and any remaining remediation conditions.

1. Inventory subscriptions, tenants, and services using Microsoft Azure AI Foundry to identify the potentially affected deployment scope.
2. Check the service update or remediation status against Microsoft's security guidance, because the record does not provide a specific fixed release.
3. If the organization can observe service-related outbound traffic, review unexpected requests originating from Azure AI Foundry, especially requests inconsistent with the service's intended operation.
4. Correlate the time of service remediation with available network and sensitive-resource access telemetry.
5. The available evidence does not define a vulnerability-specific IOC, log signature, or detection rule. The absence of an anomalous event should not be treated as proof that the service is safe.