CVE-2026-118551 in Drupal
Summary
by MITRE • 10/30/2026
Insecure Storage of Sensitive Information vulnerability in Drupal AI (Artificial Intelligence) allows Retrieve Embedded Sensitive Data. 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 Drupal AI module, which integrates artificial intelligence capabilities into the widely used content management system, contains a critical insecure storage vulnerability affecting versions ranging from 0.0.0 through 1.4.11 and 1.5.0 through 1.5.2. This flaw stems from improper data handling practices where sensitive information is persisted in an unencrypted or plaintext format within the application's local storage mechanisms, such as browser localStorage or server-side configuration files that lack adequate access controls. The vulnerability allows for the retrieval of embedded sensitive data by unauthorized actors who gain access to these storage locations. In many web applications utilizing AI modules, this can include API keys used to communicate with external machine learning services, user authentication tokens, personally identifiable information processed during inference requests, or other credentials necessary for the module's operation. The presence of such data in a retrievable state represents a significant deviation from security best practices regarding sensitive data management.
From a technical perspective, this issue aligns closely with CWE-312, which defines insecure storage of sensitive information as a flaw where critical data is stored without appropriate encryption or protection mechanisms. When the Drupal AI module stores tokens or keys in client-side storage like localStorage, these values are accessible via JavaScript running on any page within the same origin, increasing the risk if cross-site scripting vulnerabilities exist elsewhere in the site. If stored server-side, improper file permissions or lack of encryption at rest can allow local privilege escalation attacks where an attacker with limited shell access can read configuration files containing secrets. The operational impact is severe, as compromised API keys may lead to unauthorized usage of AI services resulting in financial loss through billing fraud. Furthermore, exposure of user data processed by the AI models violates privacy regulations such as GDPR or CCPA and undermines trust in the platform's ability to handle sensitive inputs securely.
This vulnerability also maps to MITRE ATT&CK technique T1530, Data from Local System Retrieval, where adversaries collect data stored on local systems for later exfiltration. By exploiting this insecure storage mechanism, an attacker can pivot further into the network if those credentials provide access to broader infrastructure components. For instance, API keys might grant write access to cloud resources or databases connected to the AI service backend. The lack of encryption means that even basic forensic analysis tools or simple script execution by a malicious user with low-level privileges on the web server or client machine can extract these secrets without needing complex exploitation techniques beyond standard file reading permissions.
Mitigation strategies must focus on immediate remediation and long-term architectural improvements. Administrators should upgrade to versions of the Drupal AI module that are not listed in the affected range, specifically targeting releases after 1.4.11 and 1.5.2 where these storage issues have been addressed by developers implementing secure defaults such as encrypted storage or removal of sensitive data from client-side persistence entirely. For environments unable to upgrade immediately, temporary mitigations include restricting file permissions on server-side configuration files containing secrets so that only the web application process user can read them, and auditing JavaScript code for any custom scripts that might inadvertently expose stored tokens via console logging or network requests. Additionally, organizations should implement strict Content Security Policy headers to mitigate potential cross-site scripting vectors that could be used in conjunction with this storage flaw to exfiltrate data. Regular rotation of all API keys and credentials associated with the AI module is essential to limit the window of exposure if a breach occurs. Future development cycles must enforce secure coding standards that mandate encryption for any sensitive data at rest, ensuring compliance with industry frameworks like OWASP Application Security Verification Standard which explicitly requires protection against insecure storage vulnerabilities through cryptographic controls and strict access management policies.