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

**Authors:** Alberto Purpura, Sahil Wadhwa, Jesse Zymet, Akshay Gupta, Andy Luo, Melissa Kazemi Rad, Swapnil Shinde, Mohammad Shahed Sorower  
**arXiv:** [2503.01742](https://arxiv.org/abs/2503.01742)  
**Date:** 2025-03-03  
**Summary page:** https://www.summarizepaper.com/en/paper/2503.01742v1/

## Key points

- The rapid growth of Large Language Models (LLMs) has revolutionized various industries, offering new possibilities for enhancing productivity and decision-making.
- Increased reliance on LLMs requires ensuring their safe and ethical use to prevent the generation of misleading or harmful content.
- Defensive research focuses on safeguarding LLMs against potential attacks, but identifying vulnerabilities beforehand remains a challenge.
- Red teaming involves proactively attacking LLMs to uncover weaknesses and enhance system security.
- Attack methods in red teaming include prompt-based attacks, jailbreak techniques, style injection, among others.
- Evaluation strategies for red teaming include human reviewers, keyword-based assessments, and utilizing LLMs as judges.
- Red teaming is essential for organizations to anticipate threats to their LLM-supported systems and mitigate risks before deployment.

## Summary

The rapid growth of Large Language Models (LLMs) has revolutionized various industries, offering new possibilities for enhancing productivity and decision-making. However, with this increased reliance on LLMs comes the critical responsibility of ensuring their safe and ethical use. LLMs are vulnerable to misuse, which can lead to the generation of misleading or harmful content, as seen in high-profile cases like Microsoft's Tay. Defensive research has focused on safeguarding LLMs against potential attacks, but identifying vulnerabilities beforehand remains a challenge. To complement defensive efforts, researchers have turned to an offensive approach known as red teaming. Red teaming involves proactively attacking LLMs to uncover weaknesses and enhance system security. This paper provides a practical overview of the LLM red teaming literature, outlining various attack methods such as prompt-based attacks, jailbreak techniques, style injection, and more. Evaluation strategies include human reviewers, keyword-based assessments, and utilizing LLMs as judges. The survey categorizes red teaming papers based on key attributes such as attack methods and evaluation approaches. By exploring different components of a red teaming system and software packages for implementation, readers can gain insights into major concepts for practical applications. Overall, red teaming is essential for organizations looking to anticipate threats to their LLM-supported systems and mitigate risks before deployment.