Association as a gift enables people do not have to mention something in completely straightforward words and allows others to understand what they intend to refer to. In this paper, we propose a chain association-based adversarial attack against natural language processing systems, utilizing the comprehension gap between humans and machines. We first generate a chain association graph for Chinese characters based on the association paradigm for building search space of potential adversarial examples. Then, we introduce an discrete particle swarm optimization algorithm to search for the optimal adversarial examples. We conduct comprehensive experiments and show that advanced natural language processing models and applications, including large language models, are vulnerable to our attack, while humans appear good at understanding the perturbed text. We also explore two methods, including adversarial training and associative graph-based recovery, to shield systems from chain association-based attack. Since a few examples that use some derogatory terms, this paper contains materials that may be offensive or upsetting to some people.

本研究针对自然语言处理系统的脆弱性，提出了一种基于链关联的对抗攻击方法，利用了人类与机器之间的理解差距。通过生成汉字的链关联图并应用粒子群优化算法，我们发现高级自然语言处理模型对这种攻击敏感，而人类则能够良好理解受到干扰的文本。同时，我们探讨了对抗训练和基于关联图的恢复两种防护方法，以提高系统的安全性。

基于链关联的自然语言处理系统攻击与防御