The article "Red-Teaming for Generative AI: Silver Bullet or Security Theater?" explores the concept of AI red-teaming as a strategy to identify and mitigate risks in generative AI (GenAI) models. While AI red-teaming is promoted as a key component of policy and corporate strategies to ensure the safety, security, and trustworthiness of GenAI, the paper highlights significant ambiguities and inconsistencies in its definition, scope, and application. The authors analyze recent AI red-teaming activities and conduct an extensive survey of relevant research to characterize the practices and criteria for AI red-teaming. They find that AI red-teaming practices vary widely in terms of purpose, artifact under evaluation, setting, and resulting decisions. The paper argues that while red-teaming may be a valuable concept for characterizing GenAI harm mitigations, its broad application as a panacea for all risks risks becoming "security theater." The authors propose a question bank to guide future AI red-teaming practices and suggest that more robust evaluation tools are needed for generative AI. They also emphasize the importance of clear definitions, diverse team compositions, and transparent disclosure of findings to ensure that red-teaming is effective and meaningful. The paper concludes that AI red-teaming is a complex and evolving field that requires careful consideration and structured approaches to address the challenges of GenAI safety and security.The article "Red-Teaming for Generative AI: Silver Bullet or Security Theater?" explores the concept of AI red-teaming as a strategy to identify and mitigate risks in generative AI (GenAI) models. While AI red-teaming is promoted as a key component of policy and corporate strategies to ensure the safety, security, and trustworthiness of GenAI, the paper highlights significant ambiguities and inconsistencies in its definition, scope, and application. The authors analyze recent AI red-teaming activities and conduct an extensive survey of relevant research to characterize the practices and criteria for AI red-teaming. They find that AI red-teaming practices vary widely in terms of purpose, artifact under evaluation, setting, and resulting decisions. The paper argues that while red-teaming may be a valuable concept for characterizing GenAI harm mitigations, its broad application as a panacea for all risks risks becoming "security theater." The authors propose a question bank to guide future AI red-teaming practices and suggest that more robust evaluation tools are needed for generative AI. They also emphasize the importance of clear definitions, diverse team compositions, and transparent disclosure of findings to ensure that red-teaming is effective and meaningful. The paper concludes that AI red-teaming is a complex and evolving field that requires careful consideration and structured approaches to address the challenges of GenAI safety and security.