CVE-2024-41572 in Learning with Texts
Summary
by MITRE • 08/21/2024
Learning with Texts (LWT) 2.0.3 is vulnerable to Cross Site Scripting (XSS). The application has a specific function that does not filter special characters in URL parameters. Remote attackers can inject JavaScript code without authorization.
If you want to get best quality of vulnerability data, you may have to visit VulDB.
Analysis
by VulDB Data Team • 03/13/2025
Learning with Texts version 2.0.3 contains a critical cross site scripting vulnerability that stems from inadequate input validation within its URL parameter handling mechanisms. This vulnerability manifests when the application processes user-supplied data through URL parameters without proper sanitization or encoding, creating an attack surface where malicious actors can inject arbitrary JavaScript code. The flaw exists specifically in the application's text processing functionality that accepts user input through web requests, failing to implement proper security controls to prevent malicious code execution. The vulnerability is classified as a classic reflected cross site scripting issue where attacker-controlled payloads are reflected back to users through the application's response. According to CWE-79, this represents a failure to sanitize input data, while the ATT&CK framework categorizes this under T1203 - Exploitation for Client Execution, highlighting how attackers can leverage such vulnerabilities to execute malicious scripts in victims' browsers. The impact of this vulnerability extends beyond simple data theft, as it allows attackers to manipulate the application's behavior, potentially leading to session hijacking, credential theft, or redirection to malicious sites. Attackers can craft specially formatted URLs containing JavaScript payloads that, when executed in a victim's browser, can perform actions such as stealing cookies, modifying application content, or redirecting users to phishing sites. The vulnerability is particularly concerning because it requires no authentication to exploit, making it accessible to any remote attacker who can convince a user to click a malicious link. The lack of proper input filtering in the URL parameter processing function creates a persistent security gap that undermines the application's overall security posture.
The technical exploitation of this vulnerability follows a standard XSS attack pattern where malicious JavaScript code is embedded within URL parameters and executed when the application processes the request. The vulnerability occurs because the application's input validation mechanism fails to properly escape or filter special characters that could be interpreted as executable code. When users navigate to URLs containing malicious payloads, the application processes these parameters without adequate sanitization, allowing the injected JavaScript to execute within the user's browser context. This creates a persistent threat vector that can be exploited across multiple user sessions, as the malicious code remains embedded in the application's response handling mechanism. The vulnerability's severity is amplified by the fact that it affects core application functionality, making it difficult to isolate and remediate without comprehensive code review. Security researchers have identified that the application's failure to implement proper content security policies and input validation creates multiple attack vectors, with the URL parameter processing function serving as the primary entry point for malicious code injection.
Organizations using Learning with Texts version 2.0.3 must implement immediate mitigation strategies to protect their users from potential exploitation. The most effective approach involves implementing proper input validation and output encoding mechanisms that sanitize all user-supplied data before processing or displaying it within the application. This includes implementing proper HTML escaping for all dynamic content and ensuring that URL parameters undergo thorough validation before being processed. Organizations should also consider implementing a content security policy that restricts script execution and prevents unauthorized code injection. The application should be updated to a patched version that addresses the specific input validation flaws, as the vendor has likely released security updates to resolve this vulnerability. Additionally, implementing web application firewalls and intrusion detection systems can help monitor for suspicious URL patterns that may indicate attempted exploitation. Regular security testing and code reviews should be conducted to identify similar vulnerabilities in other application components. The vulnerability's classification under CWE-79 and its alignment with ATT&CK techniques T1203 and T1566 highlight the need for comprehensive security measures beyond simple patching. Network administrators should also consider implementing user education programs to help identify suspicious links and reduce the likelihood of successful social engineering attacks that exploit this vulnerability. The remediation process requires careful attention to ensure that all input validation mechanisms are properly implemented and tested to prevent similar issues in other parts of the application architecture.