The article explores the concept of Large Language Model (LLM) red teaming, a practice where practitioners provide inputs to LLMs to test their boundaries and assess risks. It discusses the characteristics of LLM red teaming, including its manual, collaborative, and exploratory nature. The article also delves into the motivations behind red teaming, the strategies employed, and how the findings contribute to model security and safety.
