Improved Techniques for Optimization-Based Jailbreaking on Large Language Models
Xiaojun Jia Tianyu Pang Chao Du Yihao Huang Jindong Gu Yang Liu Xiaochun Cao Min Lin
Large language models (LLMs) are being rapidly developed, and a key component of their widespread deployment is their safety-related alignment. Many red-teaming efforts aim to jailbreak LLMs, where among these efforts, the Greedy Coordinate Gradient (GCG) attack's success has led to a growing interest in the study of optimization-based jailbreaking techniques. Although GCG is a significant milestone, its attacking efficiency remains unsatisfactory. In this paper, we present several improved (empirical) techniques for optimization-based jailbreaks like GCG. We first observe that the single target template of ”Sure'' largely limits the attacking performance of GCG; given this, we propose to apply diverse target templates containing harmful self-suggestion and/or guidance to mislead LLMs. Besides, from the optimization aspects, we propose an automatic multi-coordinate updating strategy in GCG (i.e., adaptively deciding how many tokens to replace in each step) to accelerate convergence, as well as tricks like easy-to-hard initialization. Then, we combine these improved technologies to develop an efficient jailbreak method, dubbed $\mathcal{I}$-GCG. In our experiments, we evaluate our $\mathcal{I}$-GCG on a series of benchmarks (such as NeurIPS 2023 Red Teaming Track). The results demonstrate that our improved techniques can help GCG outperform state-of-the-art jailbreaking attacks and achieve a nearly 100\% attack success rate.
The code is released at https://github.com/jiaxiaojunQAQ/I-GCG.
Jailbreaking Attacks, Large Language Models
Our paper presents I-GCG, an optimized jailbreaking method for large language models, achieving nearly 100% attack success rate by introducing diverse target templates, adaptive multi-coordinate updates, and easy-to-hard initialization.