From Guidelines to Practice: Defining Requirements for AI Red-Teaming Tools
Gia Phong Tran, Thai Duong Huynh, Randolph Loh, Balachandar Gowrisankar, Zihao Wang, Moe Thu Kyaw, Jun Sun, Adams Wai-Kin Kong 等 9 位作者
Artificial Intelligence (AI) has seen significant developments and increased usage in recent years. AI benefits various tasks, automates decision-making, and enhances efficiency. However, AI also introduces additional attack surfaces for threat actors to perform attacks like prompt injection and jailbreaking, making security a great concern. AI security testing aims to imitate adversarial attacks or malicious inputs via manual testing or with the assistance of tools. Thus, it is critical that organizations are aware of the capabilities and limitations of the tools before using them. Existing studies provide insights on the overall architecture of AI security testing tools and raise concerns on their reliability, but their analysis is limited to attack performance. Selection of a tool for AI risk assessments should also depend on their alignment with the recommendations of industry standards, but to our knowledge, previous studies have not utilized such guidelines. To bridge this gap, we analyze several industry standards and guidelines to identify the requirements of AI testing tools. We discuss and highlight desirable features across various AI security testing tools for a variety of different AI systems. We evaluated six tools across six Large Language Models, reported on our insights, and identified gaps that should be considered by future tools.
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