CVE-2025-23484 in Predict When Plugin
Summary
by MITRE • 03/03/2025
Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting') vulnerability in NotFound Predict When allows Reflected XSS. This issue affects Predict When: from n/a through 1.3.
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Analysis
by VulDB Data Team • 03/03/2025
This vulnerability represents a classic cross-site scripting flaw that exploits improper input sanitization during web page generation processes. The issue manifests as a reflected cross-site scripting vulnerability within the NotFound Predict When application, specifically impacting versions ranging from n/a through 1.3. The vulnerability stems from the application's failure to properly neutralize user-supplied input before incorporating it into dynamically generated web content, creating an avenue for malicious actors to inject arbitrary script code into web pages viewed by other users. This weakness directly aligns with CWE-79 which categorizes improper neutralization of input during web page generation as a primary contributor to cross-site scripting attacks.
The technical execution of this vulnerability occurs when user input containing malicious script code is reflected back to the victim's browser through the application's response handling mechanisms. When a user interacts with the vulnerable application and provides input that is not adequately sanitized or encoded, the malicious payload becomes embedded within the generated HTML content. The reflected nature of this vulnerability means that the malicious script is executed in the victim's browser context without requiring persistent storage on the server side, making it particularly challenging to detect and prevent. Attackers can craft malicious URLs or forms that, when submitted to the vulnerable application, will return the malicious script in the HTTP response, which is then executed by the victim's browser.
The operational impact of this vulnerability extends beyond simple script execution, as it enables attackers to perform a range of malicious activities including session hijacking, credential theft, data exfiltration, and redirection to malicious sites. An attacker could potentially steal session cookies, allowing them to impersonate legitimate users and gain unauthorized access to protected resources. The vulnerability also provides a vector for more sophisticated attacks such as defacement of web pages, injection of malicious advertisements, or creation of backdoor access points. Given that this affects the Predict When application, the impact could be particularly concerning for organizations relying on this tool for data processing or user interaction, as it could compromise sensitive information or disrupt business operations through unauthorized access to the application's functionality.
Mitigation strategies should focus on implementing robust input validation and output encoding mechanisms throughout the application's codebase. The most effective approach involves ensuring that all user-supplied input is properly sanitized and encoded before being incorporated into web page content, particularly when dealing with dynamic HTML generation. Implementing Content Security Policy headers can provide an additional layer of protection by restricting the sources from which scripts can be loaded and executed. Regular security testing including dynamic application security testing and manual penetration testing should be conducted to identify and remediate similar vulnerabilities. The application should also implement proper HTTP headers such as X-Content-Type-Options and X-Frame-Options to prevent certain types of attacks. Organizations should maintain up-to-date security patches and consider implementing web application firewalls to detect and block malicious requests targeting this vulnerability. This vulnerability maps to several ATT&CK techniques including T1566 for social engineering and T1059 for command and scripting interpreter, highlighting the need for comprehensive defensive measures across multiple security domains.