# Robust Safety Classifier for Large Language Models: Adversarial Prompt Shield

**Authors:** Jinhwa Kim, Ali Derakhshan, Ian G. Harris  
**arXiv:** [2311.00172](https://arxiv.org/abs/2311.00172)  
**Date:** 2023-10-31  
**Summary page:** https://www.summarizepaper.com/en/paper/2311.00172v1/

## Key points

- Safety is paramount in Large Language Models to prevent harmful responses
- Researchers have developed a computational model to identify and mitigate harmful outputs
- Existing models struggle with adversarial noise in inputs
- A recent study introduces a lightweight model that excels at detecting and handling adversarial prompts
- Innovative strategies like Bot Adversarial Noisy Dialogue datasets enhance the robustness of models
- Incorporating adversarial examples can reduce success rate of attacks by up to 60%
- Advancements pave the way for more reliable and resilient conversational agents
- Developers can enhance security and effectiveness of models by leveraging these advancements

## Summary

In the realm of Large Language Models, ensuring safety is paramount as these systems are susceptible to  that can lead to the generation of harmful responses. To address this concern, researchers have developed a , a computational model designed to identify and mitigate potentially harmful or offensive outputs. However, existing  often struggle when faced with inputs containing adversarial noise. In light of this challenge, a recent study introduces the , a lightweight model that excels in accurately detecting and handling adversarial prompts while demonstrating resilience against such attacks. Moreover, the research proposes innovative strategies for autonomously generating adversarial training datasets known as Bot Adversarial Noisy Dialogue (BAND) datasets. These datasets are specifically crafted to enhance the robustness of the  by exposing it to diverse adversarial examples during the training process. Through rigorous evaluations involving Large Language Models, the study showcases that incorporating these adversarial examples can significantly reduce the success rate of  by up to 60%. The advancements presented in this research pave the way for a new generation of more reliable and resilient conversational agents. By leveraging the  and utilizing Bot Adversarial Noisy Dialogue datasets, developers can enhance the security and effectiveness of Large Language Models in real-world applications. The findings underscore the importance of continuously improving safety measures in AI systems to ensure their responsible and ethical deployment across various domains.