CVE-2024-53258 in Autolab
Summary
by MITRE • 11/25/2024
Autolab is a course management service that enables auto-graded programming assignments. From Autolab versions v.3.0.0 onward students can download all assignments from another student, as long as they are logged in, using the download_all_submissions feature. This can allow for leakage of submissions to unauthorized users, such as downloading submissions from other students in the class, or even instructor test submissions, given they know their user IDs. This issue has been patched in commit `1aa4c769` which is not yet in a release version, but is expected to be included in version 3.0.3. Users are advised to either manually patch or to wait for version 3.0.3. As a workaround administrators can disable the feature.
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Analysis
by VulDB Data Team • 04/08/2025
The vulnerability identified as CVE-2024-53258 affects Autolab, a widely used course management service designed for automated grading of programming assignments. This system serves as a critical platform for educational institutions where students submit code assignments that are automatically evaluated for correctness. The security flaw manifests in the download_all_submissions feature that was introduced in Autolab version 3.0.0, creating an unauthorized access vector that compromises the integrity of student submissions and instructor test materials. The vulnerability stems from insufficient access controls and authentication checks within the system's permission model, allowing any authenticated user to potentially access submissions belonging to other users within the same course environment.
The technical implementation of this flaw involves a lack of proper authorization verification when users attempt to download submissions through the download_all_submissions functionality. Specifically, the system fails to validate whether the requesting user has legitimate access rights to the target submissions, particularly when accessing materials from other students or instructor test submissions. This represents a clear violation of the principle of least privilege and demonstrates inadequate input validation and access control mechanisms. The vulnerability enables what cybersecurity experts would classify as an unauthorized data access scenario, where users can bypass normal access restrictions to retrieve sensitive academic materials. The flaw essentially creates a path for information disclosure that violates fundamental security principles and could result in academic integrity issues, including potential cheating scenarios and unauthorized access to solution materials.
The operational impact of this vulnerability extends beyond simple data exposure, creating significant risks for educational institutions relying on Autolab for their programming course management. Students who gain access to other users' submissions could potentially plagiarize code, undermining the educational objectives of programming assignments and compromising the learning experience. Instructors who rely on test submissions to verify system functionality may find their test materials exposed to unauthorized users, potentially leading to security breaches or system compromise through malicious exploitation of test data. The vulnerability affects the confidentiality and integrity aspects of the system's security model, as it allows unauthorized data access that could be leveraged for more sophisticated attacks or academic misconduct. This issue particularly impacts the trust model of online learning platforms where students expect their submissions to remain private and secure.
The patch for this vulnerability was implemented in commit 1aa4c769 and is expected to be included in version 3.0.3 of Autolab, addressing the insufficient access controls that enabled unauthorized submission downloads. System administrators should implement immediate mitigation strategies while awaiting the official release, including disabling the download_all_submissions feature as a temporary workaround. The vulnerability aligns with CWE-284, which addresses improper access control issues, and represents a clear example of insufficient authorization checks that could be exploited by attackers following the ATT&CK technique T1213.002 for data access and T1078.004 for valid accounts usage. Organizations using Autolab should conduct thorough security assessments of their deployment, review user access permissions, and implement monitoring for unauthorized access attempts to submissions. The patch addresses the root cause by implementing proper access control validation that ensures users can only download submissions they are authorized to access, thereby restoring the system's intended security posture and protecting academic integrity within the platform.