CVE-2026-102583 in Moodle
Summary
by MITRE • 09/30/2026
A flaw was found in Moodle. An incorrect capability check in the artificial intelligence (AI) editor placement's image generation web service allows an authenticated user to invoke the feature without holding the required capability. This flaw permits unauthorized users to access and utilize the AI image generation functionality.
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Analysis
by VulDB Data Team • 09/30/2026
The identified vulnerability resides within the Moodle learning management system, specifically affecting its integrated artificial intelligence features. The core technical issue is a broken access control mechanism located in the web service responsible for handling image generation requests from the AI editor placement module. In secure software architecture, every privileged operation must be preceded by rigorous capability checks to ensure that the invoking user possesses the necessary permissions defined by the system's role-based access control policy. However, this implementation fails to validate whether the authenticated session initiating the request holds the specific capability required for image generation. Consequently, any user who has successfully logged into the Moodle instance can bypass these authorization controls and directly invoke the underlying API endpoint designed exclusively for authorized personnel or users with elevated privileges.
This flaw represents a classic example of an insecure direct object reference combined with insufficient privilege verification, aligning closely with CWE-284 Improper Access Control. By allowing unprivileged authenticated users to trigger resource-intensive AI operations, the vulnerability creates significant operational risks beyond simple unauthorized access. The primary impact involves the potential for denial-of-service conditions due to the computational expense associated with generating images via artificial intelligence models. Attackers can exploit this flaw to consume excessive server resources, such as CPU and memory, by repeatedly requesting image generation tasks. This resource exhaustion can degrade system performance for legitimate users or cause complete service unavailability if not properly rate-limited at a lower infrastructure level.
Furthermore, the exploitation of this vulnerability may lead to indirect security compromises depending on how the generated images are processed or stored. If the AI model processes user-supplied prompts that contain malicious payloads designed to exploit subsequent rendering engines or storage mechanisms, the system could be used as an initial foothold for more advanced attacks. This behavior is consistent with techniques observed in the MITRE ATT&CK framework under T1059 Command and Scripting Interpreter if the generated content triggers downstream script execution, or generally falls under unauthorized resource consumption patterns that disrupt service availability. The lack of proper capability checks means that internal administrative tools intended for specific workflows are exposed to a broader audience than intended, violating the principle of least privilege which dictates that users should only have access to the resources strictly necessary for their role.
Mitigation strategies must focus on immediate patching and architectural review. Administrators should apply the latest security updates provided by Moodle as soon as they become available, ensuring that the corrected capability checks are enforced at the application layer. In environments where patching is delayed due to stability concerns or operational constraints, network-level controls such as Web Application Firewalls can be configured to restrict access to the specific API endpoints associated with AI image generation based on user roles or IP addresses if feasible. Additionally, implementing strict rate limiting and quota management for all authenticated users can mitigate the risk of resource exhaustion attacks stemming from this flaw. Long-term remediation requires a comprehensive audit of other web services within the Moodle installation to identify similar patterns where authentication is present but authorization checks are missing or incorrectly implemented, thereby hardening the overall security posture against privilege escalation and unauthorized access attempts.