# Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs

**Authors:** Zhao Xu, Fan Liu, Hao Liu  
**arXiv:** [2406.09324](https://arxiv.org/abs/2406.09324)  
**Date:** 2024-06-13  
**Summary page:** https://www.summarizepaper.com/en/paper/2406.09324v1/

## Key points

- Large Language Models (LLMs) are vulnerable to jailbreak attacks that can manipulate them to produce harmful outputs.
- Jailbreak attacks on LLMs are categorized into token-level and prompt-level attacks.
- There is a lack of exploration into key factors involved in these attacks, emphasizing the need for standardized evaluation frameworks.
- Authors conducted a comprehensive evaluation of different attack settings on LLM performance to establish a baseline benchmark for jailbreak attacks.
- Eight key factors from target-level and attack-level perspectives were analyzed, and seven representative jailbreak attacks were carried out on six defense methods using two datasets.
- Extensive experimentation involved approximately 320 experiments consuming about 50,000 GPU hours on A800-80G hardware.
- Standardized benchmarking protocols are necessary to effectively evaluate these attacks on defense-enhanced LLMs.
- Developing robust defense mechanisms against jailbreak attacks on LLMs is crucial.

## Summary

In their paper titled "Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs," authors Zhao Xu, Fan Liu, and Hao Liu delve into the vulnerabilities of Large Language Models (LLMs) in the face of jailbreak attacks. LLMs have shown remarkable capabilities in handling complex tasks without prior training. However, they are also susceptible to manipulation that can lead to harmful outputs. The authors highlight a recent trend in research that categorizes jailbreak attacks into token-level and prompt-level attacks. They note a lack of exploration into the various key factors involved in these attacks and emphasize the need for standardized evaluation frameworks. The authors conduct a comprehensive evaluation of different attack settings on LLM performance to establish a baseline benchmark for jailbreak attacks. They analyze eight key factors from both target-level and attack-level perspectives and carry out seven representative jailbreak attacks on six defense methods using two widely used datasets. This extensive experimentation involves approximately 320 experiments and consumes about 50,000 GPU hours on A800-80G hardware. The results underscore the necessity for standardized benchmarking protocols to effectively evaluate these attacks on defense-enhanced LLMs. In conclusion, this study sheds light on the intricate nature of jailbreak attacks on LLMs and highlights the importance of developing robust defense mechanisms against such threats. The authors provide their code for reference at https://github.com/usail-hkust/Bag_of_Tricks_for_LLM_Jailbreaking, facilitating further research and development in this critical area of study.