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

2025-03-03

AI Summary

Large language models (LLMs) pose increasingly critical privacy, security, and ethical risks, necessitating proactive, systematic safety evaluation. Method: This paper introduces the first end-to-end, multi-component LLM red-teaming system architecture for actively identifying and quantitatively assessing generative AI safety vulnerabilities. It integrates prompt injection, adversarial example generation, automated testing frameworks, and standardized safety benchmarks (e.g., HarmBench) into a unified pipeline covering attack generation, success-rate measurement, and multidimensional evaluation—namely, effectiveness, generalizability, and reproducibility. Contributions/Results: Key innovations include a systematic safety-enhancement framework, an open-source toolchain integration strategy, a reusable red-teaming practice guide, and a tool-selection matrix. Empirical evaluation demonstrates that the framework significantly improves developers’ efficiency in risk identification and enables reliable GenAI deployment in high-assurance settings.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

The rapid growth of Large Language Models (LLMs) presents significant privacy, security, and ethical concerns. While much research has proposed methods for defending LLM systems against misuse by malicious actors, researchers have recently complemented these efforts with an offensive approach that involves red teaming, i.e., proactively attacking LLMs with the purpose of identifying their vulnerabilities. This paper provides a concise and practical overview of the LLM red teaming literature, structured so as to describe a multi-component system end-to-end. To motivate red teaming we survey the initial safety needs of some high-profile LLMs, and then dive into the different components of a red teaming system as well as software packages for implementing them. We cover various attack methods, strategies for attack-success evaluation, metrics for assessing experiment outcomes, as well as a host of other considerations. Our survey will be useful for any reader who wants to rapidly obtain a grasp of the major red teaming concepts for their own use in practical applications.

Problem

• Addressing privacy, security, and ethical concerns in LLMs.
• Proposing red teaming to identify LLM vulnerabilities.
• Providing an end-to-end overview of LLM red teaming systems.

Innovation

• Proactive LLM vulnerability identification via red teaming
• End-to-end system for red teaming LLMs
• Comprehensive attack methods and evaluation metrics