# RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content

**Authors:** Zhuowen Yuan, Zidi Xiong, Yi Zeng, Ning Yu, Ruoxi Jia, Dawn Song, Bo Li  
**arXiv:** [2403.13031](https://arxiv.org/abs/2403.13031)  
**Date:** 2024-03-19  
**Summary page:** https://www.summarizepaper.com/en/paper/2403.13031v1/

## Key points

- Recent advancements in Large Language Models (LLMs) have shown impressive capabilities across various tasks in different domains.
- Challenges arise from biases and potential harmful content generation in LLMs, especially under malicious inputs.
- Existing mitigation strategies lack resilience against adversarial attacks.
- RigorLLM is introduced as a novel framework to moderate harmful and unsafe inputs and outputs for LLMs efficiently and effectively.
- The framework incorporates energy-based training data augmentation, optimizing safe suffix for inputs, and a fusion-based model combining robust KNN with LLMs.
- RigorLLM enhances detection capabilities and improves resilience against jailbreaking attacks, surpassing existing baselines like OpenAI API and Perspective API.
- Two primary approaches in harmful content mitigation initiatives are alignment-based and moderation-based mitigations.
- RigorLLM builds upon moderation-based techniques to develop an adversarial-resistant moderation framework that enhances safety measures for LLMs.

## Summary

Recent advancements in Large Language Models (LLMs) have demonstrated impressive capabilities across various tasks in different domains. However, the emergence of biases and the potential for generating harmful content in LLMs, especially under malicious inputs, present significant challenges. Existing mitigation strategies lack resilience against adversarial attacks. In response to these challenges, this paper introduces Resilient Guardrails for Large Language Models (RigorLLM), a novel framework designed to efficiently and effectively moderate harmful and unsafe inputs and outputs for LLMs. The framework of RigorLLM incorporates a multi-faceted approach that includes energy-based training data augmentation through Langevin dynamics, optimizing a safe suffix for inputs via minimax optimization, and integrating a fusion-based model combining robust KNN with LLMs based on the data augmentation technique. RigorLLM offers a robust solution to harmful content moderation by enhancing detection capabilities and improving resilience against jailbreaking attacks. Experimental evaluations demonstrate that RigorLLM surpasses existing baselines such as OpenAI API and Perspective API in detecting harmful content while exhibiting unparalleled resilience to jailbreaking attacks. The innovative use of constrained optimization and a fusion-based guardrail approach represents a significant advancement in developing more secure and reliable LLMs. RigorLLM sets a new standard for content moderation frameworks in the face of evolving digital threats. In the realm of harmful content mitigation initiatives, two primary approaches have emerged: alignment-based and moderation-based mitigations. Alignment-based techniques aim to align LLMs with ethical standards by training models to refuse engagement with predefined harmful topics. While these methods have shown progress, they require substantial computational resources and struggle with addressing new or evolving threats effectively. On the other hand, moderation-based mitigations focus on improving social media safety through traditional methods like OpenAI Content Moderation API and Perspective API. These classifiers operate on categorically labeled content but are limited by their label dictionary categories' effectiveness against emerging risks such as fraud and illegal activities. RigorLLM builds upon moderation-based techniques to develop an adversarial-resistant moderation framework that enhances safety measures for LLMs. By incorporating energy-based data generation, resilient optimization techniques, aggregation strategies, and probabilistic modeling approaches, RigorLLM aims to provide comprehensive protection against undesired content while maintaining robustness against sophisticated jailbreak attacks. Overall, RigorLLM represents a significant step forward in enhancing the security and reliability of Large Language Models for content moderation purposes amidst evolving digital threats.