This paper provides a practical framework for red teaming Large Language Models (LLMs) to systematically identify and address security vulnerabilities before deployment.

• Complements defensive approaches with offensive security techniques specifically designed for LLMs
• Presents a comprehensive overview of the current red teaming literature and methodologies
• Offers a structured approach for organizations to implement LLM red teaming in their GenAI application development
• Helps address critical privacy, security, and ethical concerns that emerge as LLM capabilities grow

As LLMs become more powerful and widely deployed, proactive security testing becomes essential for responsible AI development and deployment. This research provides security teams with practical guidance to strengthen defenses against potential exploits and misuse.

Building Safe GenAI Applications: An End-to-End Overview of Red Teaming for Large Language Models