  Textual adversarial samples play important roles in multiple subfields of NLP
research, including security, evaluation, explainability, and data
augmentation. However, most work mixes all these roles, obscuring the problem
definitions and research goals of the security role that aims to reveal the
practical concerns of NLP models. In this paper, we rethink the research
paradigm of textual adversarial samples in security scenarios. We discuss the
deficiencies in previous work and propose our suggestions that the research on
the Security-oriented adversarial NLP (SoadNLP) should: (1) evaluate their
methods on security tasks to demonstrate the real-world concerns; (2) consider
real-world attackers' goals, instead of developing impractical methods. To this
end, we first collect, process, and release a security datasets collection
Advbench. Then, we reformalize the task and adjust the emphasis on different
goals in SoadNLP. Next, we propose a simple method based on heuristic rules
that can easily fulfill the actual adversarial goals to simulate real-world
attack methods. We conduct experiments on both the attack and the defense sides
on Advbench. Experimental results show that our method has higher practical
value, indicating that the research paradigm in SoadNLP may start from our new
benchmark. All the code and data of Advbench can be obtained at
\url{https://github.com/thunlp/Advbench}.
