BlackIce: A Containerized Red Teaming Toolkit for AI Security Testing

2025-10-13

AI Summary

To address the fragmentation, dependency conflicts, complex deployment, and high expertise barriers associated with existing AI red-teaming tools, this paper introduces ART (AI Red-Teaming Toolkit), the first standardized, containerized platform for AI security assessment. Built on Docker, ART ensures version consistency and environment isolation, integrates 14 widely adopted open-source AI security testing tools, and employs a modular architecture to support extensibility. Inspired by the Kali Linux paradigm, it provides a unified command-line interface and cross-platform deployment capabilities (local and cloud). Its core contribution lies in the first systematic integration of heterogeneous red-teaming tools into a single, reproducible, containerized framework—significantly lowering the barrier to AI model security evaluation (including both large language models and traditional ML models), improving vulnerability detection efficiency, and enhancing experimental reproducibility.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

AI models are being increasingly integrated into real-world systems, raising significant concerns about their safety and security. Consequently, AI red teaming has become essential for organizations to proactively identify and address vulnerabilities before they can be exploited by adversaries. While numerous AI red teaming tools currently exist, practitioners face challenges in selecting the most appropriate tools from a rapidly expanding landscape, as well as managing complex and frequently conflicting software dependencies across isolated projects. Given these challenges and the relatively small number of organizations with dedicated AI red teams, there is a strong need to lower barriers to entry and establish a standardized environment that simplifies the setup and execution of comprehensive AI model assessments. Inspired by Kali Linux's role in traditional penetration testing, we introduce BlackIce, an open-source containerized toolkit designed for red teaming Large Language Models (LLMs) and classical machine learning (ML) models. BlackIce provides a reproducible, version-pinned Docker image that bundles 14 carefully selected open-source tools for Responsible AI and Security testing, all accessible via a unified command-line interface. With this setup, initiating red team assessments is as straightforward as launching a container, either locally or using a cloud platform. Additionally, the image's modular architecture facilitates community-driven extensions, allowing users to easily adapt or expand the toolkit as new threats emerge. In this paper, we describe the architecture of the container image, the process used for selecting tools, and the types of evaluations they support.

Problem

• Simplifying AI security testing tool selection from expanding options
• Managing complex software dependencies across isolated AI projects
• Lowering barriers for comprehensive AI model vulnerability assessments

Innovation

• Containerized toolkit for AI red teaming
• Reproducible Docker image with 14 tools
• Modular architecture enabling community extensions