CVE-2026-63239 in Koollab
Summary
by MITRE • 07/29/2026
A hard-coded AWS IAM credentials vulnerability in Koollab LMS allowed an attacker to access shared multi-tenant S3 buckets and SQS queues, exposing sensitive data and enabling malicious content injection, job manipulation, or email interception.
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Analysis
by VulDB Data Team • 07/29/2026
This vulnerability represents a critical security flaw in the Koollab Learning Management System where hard-coded AWS Identity and Access Management credentials were embedded within the application codebase. The presence of such credentials creates an inherent risk that aligns with CWE-798, which specifically addresses the use of hard-coded credentials in software systems. When credentials are hardcoded rather than dynamically retrieved from secure configuration management systems, they become permanently exposed regardless of access controls or authentication mechanisms. This particular flaw allowed unauthorized parties to gain direct access to the underlying AWS infrastructure components that support the multi-tenant architecture of the LMS platform.
The technical implementation of this vulnerability stems from poor security practices in credential management within the application's deployment configuration. Attackers who discovered these hard-coded credentials could leverage them to establish legitimate connections to the shared S3 storage buckets and Simple Queue Service (SQS) queues that host sensitive educational data. The multi-tenant nature of the platform meant that compromised access could potentially extend across multiple organizations using the same LMS instance, creating a widespread impact scenario. This vulnerability operates at the intersection of credential security and cloud infrastructure exposure, where the hardcoded keys bypass normal authentication procedures entirely.
The operational impact of this vulnerability extends beyond simple data theft to encompass active exploitation capabilities within the platform's distributed architecture. Attackers could manipulate job processing through SQS queue manipulation, potentially disrupting educational services or injecting malicious content into the learning environment. Email interception capabilities would allow for surveillance of communications between instructors and students, while access to S3 buckets could result in data exfiltration, modification of course materials, or complete compromise of student information. The severity of this exposure places it within the ATT&CK framework under T1566 (Phishing) and T1046 (Network Service Scanning) as attackers could use these credentials to expand their reconnaissance efforts.
Mitigation strategies for this vulnerability must address both immediate remediation and long-term architectural improvements. The primary action involves removing all hardcoded credentials from the source code and implementing proper secret management solutions such as AWS Systems Manager Parameter Store or AWS Secrets Manager. Organizations should enforce principle of least privilege access controls, ensuring that the IAM roles associated with the application have minimal necessary permissions to perform their functions. Regular security audits and code reviews should be implemented to prevent future occurrences of hardcoded credentials. Additionally, continuous monitoring of credential usage through AWS CloudTrail logs and implementing automated alerting systems can help detect unauthorized access attempts. The remediation process should also include reissuing new credentials and rotating existing ones to ensure that any previously compromised keys are no longer valid, aligning with security best practices established in NIST SP 800-53 and ISO/IEC 27001 standards for secure credential management and access control.