CVE-2024-52584 in Autolab
Summary
by MITRE • 11/18/2024
Autolab is a course management service that enables auto-graded programming assignments. There is a vulnerability in version 3.0.1 where CAs can view or edit the grade for any submission ID, even if they are not a CA for the class that has the submission. The endpoints only check that the CAs have the authorization level of a CA in the class in the endpoint, which is not necessarily the class the submission is attached to. Version 3.0.2 contains a patch. No known workarounds are available.
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Analysis
by VulDB Data Team • 01/22/2025
The vulnerability identified as CVE-2024-52584 affects Autolab version 3.0.1, a course management platform designed for automated grading of programming assignments. This system serves educational institutions by facilitating the creation, distribution, and automatic evaluation of programming exercises. The flaw represents a critical authorization bypass issue that undermines the fundamental security model of the platform, potentially allowing unauthorized users to access and modify academic records across different course sections.
The technical flaw stems from insufficient access control validation within the platform's grading endpoints. Specifically, the system's authorization checks only verify that a user possesses the required CA (Course Assistant) level credentials for their own class, without confirming that the user has legitimate access to the specific class associated with the target submission. This design oversight creates a privilege escalation vulnerability where any user with CA-level access in one class can manipulate grade records for submissions in unrelated classes. The vulnerability manifests at the application logic level, representing a classic case of inadequate input validation and access control enforcement.
The operational impact of this vulnerability extends beyond simple data integrity concerns, potentially compromising the academic integrity of multiple courses simultaneously. Course assistants who should only have access to their own class assignments could theoretically modify grades for submissions in other courses, leading to unauthorized grade changes, academic dishonesty, and potential grade manipulation. This flaw affects the core functionality of the platform's security model, as it allows unauthorized access to grade data that should remain isolated between different course sections. The implications include potential academic fraud, loss of trust in automated grading systems, and disruption of educational processes across multiple classes.
This vulnerability aligns with CWE-285, which addresses improper authorization within software systems, and demonstrates characteristics consistent with ATT&CK technique T1548.003, which covers abuse of cloud platforms for privilege escalation. The issue represents a failure in implementing proper access control mechanisms and could be classified as a privilege escalation vulnerability that allows lateral movement between different course contexts. Organizations using Autolab should immediately upgrade to version 3.0.2, as no effective workarounds exist for this authorization bypass flaw. The patch addresses the core access control logic by ensuring that CA privileges are validated against the specific class associated with each submission rather than merely checking for general CA status. Security administrators should also review access logs for potential unauthorized access patterns and consider implementing additional monitoring for grade modification activities across different course sections to detect any exploitation attempts that may have occurred before the patch was applied.