The blog post discusses the importance of red teaming in understanding and securing AI systems based on Large Language Models (LLMs), highlighting that these models introduce new security challenges distinct from traditional software vulnerabilities. It explains how LLMs, unlike conventional systems, can be manipulated through data inputs, turning them into attack vectors without direct system access. The article provides examples, such as adversarial SEO attacks and LLM-targeted exploits, to illustrate these vulnerabilities. It emphasizes the need for an advanced automated red teaming agent that surpasses human capabilities in identifying and exploiting weaknesses in AI applications, aiming to enhance security and trust in AI systems. The series aims to explore these challenges, define new vulnerabilities, and develop benchmarks to assess red teaming effectiveness, while acknowledging that traditional cybersecurity methods remain relevant but insufficient for the unique threats posed by LLMs.