  Although Large Language Models (LLMs) have demonstrated significant
capabilities in executing complex tasks in a zero-shot manner, they are
susceptible to jailbreak attacks and can be manipulated to produce harmful
outputs. Recently, a growing body of research has categorized jailbreak attacks
into token-level and prompt-level attacks. However, previous work primarily
overlooks the diverse key factors of jailbreak attacks, with most studies
concentrating on LLM vulnerabilities and lacking exploration of
defense-enhanced LLMs. To address these issues, we evaluate the impact of
various attack settings on LLM performance and provide a baseline benchmark for
jailbreak attacks, encouraging the adoption of a standardized evaluation
framework. Specifically, we evaluate the eight key factors of implementing
jailbreak attacks on LLMs from both target-level and attack-level perspectives.
We further conduct seven representative jailbreak attacks on six defense
methods across two widely used datasets, encompassing approximately 320
experiments with about 50,000 GPU hours on A800-80G. Our experimental results
highlight the need for standardized benchmarking to evaluate these attacks on
defense-enhanced LLMs. Our code is available at
https://github.com/usail-hkust/Bag_of_Tricks_for_LLM_Jailbreaking.
