Demo: ViolentUTF as An Accessible Platform for Generative AI Red Teaming
Tam n. Nguyen
TL;DR
The paper addresses the accessibility barrier in GenAI red teaming, where existing tools are technically demanding and poorly suited for collaborative risk assessment. It presents Violent UTF, a unified platform that integrates PyRIT, Garak, and Ollabench with GUI/CLI/API access and a secure architecture to enable both non-technical domain experts and security researchers. Key contributions include standardized abstractions (Generators, Prompts, Converters, Evaluators, Orchestrators, Memory), integration of diverse RT and human-centric evaluation tools, and a cross-domain reasoning demonstration. The work demonstrates practical impact for evaluating LLMs in cybersecurity contexts and highlights the platform's potential to democratize rigorous, cross-domain red teaming.
Abstract
The rapid integration of Generative AI (GenAI) into various applications necessitates robust risk management strategies which includes Red Teaming (RT) - an evaluation method for simulating adversarial attacks. Unfortunately, RT for GenAI is often hindered by technical complexity, lack of user-friendly interfaces, and inadequate reporting features. This paper introduces Violent UTF - an accessible, modular, and scalable platform for GenAI red teaming. Through intuitive interfaces (Web GUI, CLI, API, MCP) powered by LLMs and for LLMs, Violent UTF aims to empower non-technical domain experts and students alongside technical experts, facilitate comprehensive security evaluation by unifying capabilities from RT frameworks like Microsoft PyRIT, Nvidia Garak and its own specialized evaluators. ViolentUTF is being used for evaluating the robustness of a flagship LLM-based product in a large US Government department. It also demonstrates effectiveness in evaluating LLMs' cross-domain reasoning capability between cybersecurity and behavioral psychology.