CVE-2023-49172 in BrainCert Plugin
Summary
by MITRE • 12/14/2023
Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting') vulnerability in BrainCert BrainCert – HTML5 Virtual Classroom allows Reflected XSS.This issue affects BrainCert – HTML5 Virtual Classroom: from n/a through 1.30.
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Analysis
by VulDB Data Team • 01/11/2024
The vulnerability identified as CVE-2023-49172 represents a critical cross-site scripting weakness within the BrainCert HTML5 Virtual Classroom platform, specifically manifesting as a reflected XSS flaw in the web page generation process. This security defect stems from inadequate input sanitization during the dynamic content creation phase, where user-supplied data is directly incorporated into web responses without proper neutralization mechanisms. The vulnerability exists within the application's handling of HTTP parameters or request inputs that are subsequently rendered in HTML output, creating an attack vector where malicious scripts can be injected and executed within the context of other users' browsers. The affected version range spans from an unspecified initial state through version 1.30, indicating this flaw has persisted across multiple releases and likely represents a long-standing architectural issue in the platform's input processing pipeline.
The technical exploitation of this reflected XSS vulnerability occurs when an attacker crafts malicious input containing script code that gets processed and returned in the web application's response. When legitimate users view the affected page containing the malicious payload, their browsers execute the injected scripts, potentially leading to session hijacking, credential theft, or unauthorized actions performed on behalf of the victim. The reflected nature of this vulnerability means that the malicious script is not stored on the server but rather injected through the current HTTP request, making it particularly dangerous as it can be delivered via email links, chat messages, or other social engineering vectors. This type of vulnerability is categorized under CWE-79 as "Improper Neutralization of Input During Web Page Generation," which specifically addresses the failure to properly sanitize user input before incorporating it into dynamically generated web content.
The operational impact of this vulnerability extends beyond simple script execution, as it fundamentally compromises the security posture of the BrainCert virtual classroom environment. Educational institutions and organizations relying on this platform face significant risks including unauthorized access to student and instructor data, potential data breaches, and the possibility of malicious actors disrupting classroom sessions or gaining administrative privileges. The vulnerability undermines the trust model of the virtual learning environment, as users cannot safely interact with the platform without risking their session integrity. From an attacker's perspective, this flaw provides a straightforward entry point for conducting phishing attacks, stealing session cookies, or redirecting users to malicious sites, making it particularly attractive for threat actors targeting educational institutions. The reflected nature of the vulnerability also means that detection and mitigation efforts must be continuously monitored, as attackers can easily craft new payloads for each attack attempt.
Mitigation strategies for CVE-2023-49172 should prioritize immediate implementation of proper input validation and output encoding mechanisms throughout the application's request handling pipeline. The platform must implement comprehensive sanitization of all user-supplied inputs before any processing or rendering occurs, utilizing established libraries and frameworks designed specifically for XSS prevention. Organizations should deploy Content Security Policy headers to limit the execution of unauthorized scripts and implement proper HTTP response headers to prevent script injection. The application should utilize context-appropriate encoding mechanisms such as HTML entity encoding for output rendering, and implement strict input validation that rejects or sanitizes potentially malicious content before it can be processed. Additionally, regular security assessments and code reviews should be conducted to identify similar vulnerabilities in other parts of the application, with the implementation of automated security scanning tools to continuously monitor for regressions or new XSS vulnerabilities. The remediation efforts should align with industry best practices for web application security and align with ATT&CK framework techniques related to credential access and privilege escalation through web application vulnerabilities.