CVE-2026-13234 in Drupal
Summary
by MITRE • 07/11/2026
Improper Neutralization of Input During Web Page Generation ("Cross-site Scripting") vulnerability in Drupal AI (Artificial Intelligence) allows Cross-Site Scripting (XSS). This issue affects AI (Artificial Intelligence) versions: from 0.0.0 to 1.2.17, from 1.3.0 to 1.3.8, from 1.4.0 to 1.4.3.
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Analysis
by VulDB Data Team • 07/11/2026
This vulnerability represents a classic cross-site scripting flaw that compromises the security of Drupal AI implementations through improper input sanitization during web page generation processes. The issue stems from insufficient validation and neutralization of user-supplied data before it is rendered in web interfaces, creating opportunities for malicious actors to inject persistent or reflected scripts that execute in users' browsers. The vulnerability affects multiple version ranges within the Drupal AI module, specifically from 0.0.0 through 1.2.17, 1.3.0 through 1.3.8, and 1.4.0 through 1.4.3, indicating a widespread impact across the module's release history.
The technical flaw manifests when user input intended for AI processing or display is not properly escaped or filtered before being incorporated into web page content. This allows attackers to embed malicious javascript code or other script payloads that execute in the context of authenticated users' browsers. The vulnerability aligns with CWE-79, which specifically addresses cross-site scripting weaknesses in web applications. When exploited, this flaw enables attackers to perform various malicious activities including session hijacking, credential theft, defacement of content, and potentially full account compromise of affected users.
The operational impact of this vulnerability extends beyond simple data corruption or display issues. Attackers can leverage this XSS weakness to establish persistent footholds within the Drupal AI environment, potentially accessing sensitive AI configurations, user data, or system resources. The attack surface is particularly concerning given that AI modules often process and display complex data sets that may contain user information or system metadata. This vulnerability can be exploited through various vectors including user profile inputs, content submission forms, or API endpoints that feed into AI processing modules.
Security mitigation strategies should prioritize immediate patching of affected Drupal AI versions to the latest stable releases that contain proper input sanitization measures. Organizations should implement comprehensive content security policies and employ strict output encoding for all user-provided data before rendering in web contexts. Additionally, regular security audits and penetration testing of AI modules should be conducted to identify similar input validation gaps. The vulnerability demonstrates the critical importance of input sanitization and output encoding practices as outlined in the OWASP Top Ten and ATT&CK framework's T1203 technique for exploitation through cross-site scripting vulnerabilities. Network segmentation and web application firewalls can provide additional defense-in-depth measures while ensuring proper access controls are implemented to limit potential damage from successful exploitation attempts.