Abhisek Mazumdar

Stored Cross-site Scripting (XSS) occurs due to improper neutralization of input during web page generation. The AI Automator submodule, which enables automatic field population based on other fields, fails to sufficiently verify access to text formats. This allows users to author field data using text formats they are not authorized to use.

Improper neutralization of input during web page generation allows for Cross-Site Scripting (XSS). The module does not sufficiently restrict HTML rendering, which enables the execution of malicious scripts under certain conditions. This issue is mitigated by the requirement that an attacker must have access to a text format capable of rendering HTML.

An issue in the AI Chatbot submodule occurs when user input is not sufficiently filtered before being rendered on a web page, leading to Cross-Site Scripting (XSS). This occurs under certain configurations and requires the attacker to have permissions to administer blocks.

Improper neutralization of input during web page generation allows for Cross-Site Scripting (XSS). The module does not sufficiently validate Markdown links under certain conditions, which can lead to the execution of malicious scripts in the user's browser.

Insecure storage of sensitive information occurs when audio files for speech-to-text requests are written to a single file in the temporary directory and are not deleted after processing. This allows an attacker with read access to the site's temporary directory—potentially via shared hosting or other code exposing temporary files—to retrieve audio data sent by other users for transcription.

Improper neutralization of input during web page generation allows Cross-Site Scripting (XSS). The module and specific submodules, including AI Automators, AI Translate, AI API Explorer, and AI Content Suggestions, enable the use of a Large Language Model (LLM) to generate HTML or Markdown for browser preview. Under certain conditions, rendering this HTML can lead to XSS or the exposure of secret communications within the context of the LLM request. This issue requires an attacker to be able to inject text into prompts to execute the attack.

An incorrect authorization issue exists in Drupal AI (Artificial Intelligence) that allows for resource injection. The module and certain submodules (AI Automators, AI Translate, AI API Explorer, AI Content Suggestions) allow the use of a Large Language Model (LLM) to generate HTML or Markdown, and preview it in a browser. Under specific conditions, rendering this HTML can expose secret communications related to the LLM request.

The issue is related to the segment cloning functionality in Mautic, allowing any authenticated user to clone segments without proper authorization checks due to a missing authorization vulnerability in the cloneAction of the segment management. This enables an authenticated user to bypass intended permission restrictions and clone segments even if they lack the necessary permissions to create new ones.