Witty Gerbil Chrome Extension: A Browser-Based Framework for Comprehensive AI System Evaluation
Year: 2025
Type: conference-paper
Abstract: Many AI red-teaming frameworks target Large Language Models (LLMs) and other generative systems via direct access or through APIs. These approaches do not always reflect the complexities of real-world deployments. Production AI applications often incorporate content moderation, guardrails, user interface constraints, and other filtering mechanisms, which can alter both user inputs and system outputs. To capture the full range of vulnerabilities, we present Witty Gerbil Chrome Extension, a browser-based testing solution coupled with a Python orchestration backend. By automating AI interactions directly in the browser, our framework preserves production safeguards and transforms, providing a more realistic picture of overall system risk. This paper outlines the architecture, operation modes, and limitations of this extension, emphasizing the importance of holistic AI evaluations that include user-facing layers in addition to core model testing.
Author: Samuel Cameron
Institution: Wake Forest University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4411549773