CVE-2026-114389 in LMS
Summary
by MITRE • 10/22/2026
Chamilo LMS from 2.0.0-alpha.4 before 2.0.0-RC.3 contains a missing authorization vulnerability in AiController::openAnswerGrade that allows any authenticated user to obtain AI grading of other students' exam attempts. Attackers can enumerate exeId and questionId values sent to the open_answer_grade endpoint to expose other students' answers through feedback and consume AI quota.
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Analysis
by VulDB Data Team • 10/22/2026
The Chamilo Learning Management System, specifically versions ranging from 2.0.0-alpha.4 up to but not including 2.0.0-RC.3, suffers from a critical missing authorization vulnerability within the AiController::openAnswerGrade method. This flaw represents a fundamental failure in access control mechanisms where the application fails to verify that the authenticated user initiating the request is authorized to view or process data belonging to other entities. In this specific context, the system does not adequately validate whether the student requesting AI grading for an exam attempt actually owns that particular attempt. Consequently, any authenticated user can manipulate input parameters to access and retrieve sensitive academic information associated with their peers.
The technical exploitation of this vulnerability relies on the ability to enumerate critical identifiers such as exeId, which represents the specific examination instance or exercise ID, and questionId, which identifies individual questions within those exams. By systematically iterating through these values sent to the open_answer_grade endpoint, an attacker can bypass intended security boundaries. The application processes these requests without checking if the current session's user context matches the owner of the exam attempt referenced by the provided IDs. This lack of object-level authorization allows the attacker to receive AI-generated feedback and grading results for other students' submissions, effectively exposing private academic performance data that should remain confidential between the student and the instructor or system administrators.
The operational impact of this vulnerability is significant from both a privacy and an integrity perspective. The primary consequence is the unauthorized disclosure of personally identifiable information related to academic performance, including specific answers provided by other students and their corresponding grades. This breach violates principles of data confidentiality and can lead to severe consequences for affected institutions regarding compliance with educational privacy regulations such as FERPA in the United States or GDPR in Europe. Furthermore, the vulnerability enables resource abuse, allowing attackers to consume AI processing quotas intended for legitimate use cases. This denial-of-service aspect degrades system performance for all users and incurs unnecessary costs associated with API usage if external AI services are billed per request.
This issue aligns closely with CWE-284, which describes Improper Access Control, specifically highlighting the failure to enforce proper authorization checks on sensitive operations. From a threat modeling perspective using the MITRE ATT&CK framework, this vulnerability facilitates unauthorized data access and can be categorized under techniques related to Collection of Data from Local System or Exfiltration Over C2 Channel if combined with other exfiltration methods. The ability to enumerate resources also touches upon CWE-200, Information Exposure, as it allows attackers to discover valid identifiers through error responses or successful retrievals of data belonging to others.
To mitigate this vulnerability, developers must implement strict object-level authorization checks within the AiController::openAnswerGrade method before processing any grading requests. The system should verify that the authenticated user's ID matches the owner ID associated with the exeId and questionId provided in the request payload. Additionally, implementing rate limiting on the open_answer_grade endpoint can help mitigate the risk of quota consumption attacks by preventing rapid enumeration attempts. Regular security audits focusing on access control logic are essential to ensure that all API endpoints properly validate user permissions against the resources being accessed. Updating Chamilo LMS to version 2.0.0-RC.3 or later is necessary as these versions contain patches addressing this specific authorization flaw.