CVE-2026-118553 in Drupal
Summary
by MITRE • 10/30/2026
Improper Neutralization of Input During Web Page Generation ("Cross-site Scripting") vulnerability in Drupal AI (Artificial Intelligence) allows Stored XSS. This issue affects AI (Artificial Intelligence) versions: from 0.0.0 to 1.4.11, from 1.5.0 to 1.5.2.
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Analysis
by VulDB Data Team • 10/30/2026
The vulnerability identified as Improper Neutralization of Input During Web Page Generation represents a critical security flaw within the Drupal AI module, specifically affecting versions ranging from 0.0.0 through 1.4.11 and from 1.5.0 to 1.5.2. This issue is classified under Cross-site Scripting, commonly abbreviated as XSS, which remains one of the most prevalent web application security risks according to industry standards such as CWE-79. The core technical flaw lies in the module's failure to properly sanitize or encode user-supplied input before it is rendered within the context of a web page generated by Drupal. When an attacker submits malicious script content through any interface exposed by the AI module, the system processes this input without adequate validation, allowing the embedded JavaScript code to be stored directly on the server-side database rather than being executed transiently in a single request.
This specific manifestation is categorized as Stored XSS because the injected payload persists within the application's data store. Unlike reflected XSS where the malicious script must be delivered via a crafted link or header, this vulnerability allows an attacker to plant code that will execute automatically whenever any user interacts with the affected part of the website. The operational impact is severe and multifaceted. Once executed in the victim's browser context, typically under the privileges of their session cookie, the malicious script can perform actions on behalf of the authenticated user. This includes stealing sensitive data such as administrative credentials or personal information, hijacking active sessions to impersonate legitimate users, defacing the website interface, or redirecting victims to phishing sites designed to harvest further credentials.
From a tactical perspective aligned with the MITRE ATT&CK framework for enterprise security, this vulnerability facilitates techniques associated with Collection and Credential Access. Attackers can leverage the stored script to exfiltrate cookies containing session tokens, thereby gaining unauthorized access to privileged accounts within the Drupal environment. The persistence of the payload means that even after the initial attack vector is discovered and patched in other areas, previously exposed pages may continue to serve malicious content until cached copies are cleared or specific cleanup procedures are executed by system administrators. This creates a prolonged window of exposure where any visitor to the compromised page becomes a potential victim without their knowledge or consent.
Mitigation strategies must address both immediate remediation and long-term defensive posture improvements. The primary corrective action is to upgrade the Drupal AI module to version 1.4.12 or later, as well as versions 1.5.3 and above, where the input handling mechanisms have been corrected to properly neutralize special characters and encode output according to context-specific rules. For organizations unable to immediately patch due to compatibility constraints, implementing a Web Application Firewall with specific rule sets capable of detecting and blocking XSS payloads in POST requests can provide temporary protection. Additionally, developers should review any custom code interacting with the AI module's API endpoints to ensure that all user inputs are strictly validated against expected formats and types before processing. Enabling Content Security Policy headers on the web server further restricts the execution environment for scripts, limiting the damage even if a bypass occurs by preventing inline script execution unless explicitly whitelisted. Regular security audits and code reviews focusing on data flow from input sources to output sinks are essential to prevent similar vulnerabilities in future development cycles.