Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs
<|reference_start|>Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs: 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 354 experiments with about 55,000 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.<|reference_end|>
@article{xu2024bag,
    title={Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs},
    author={Zhao Xu, Fan Liu, Hao Liu},
    journal={arXiv preprint arXiv:2406.09324},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09324},
    primaryClass={cs.CR cs.AI cs.CL}
}