Punctuation Matters! Stealthy Backdoor Attack for Language Models
Xuan Sheng Zhicheng Li Zhaoyang Han Xiangmao Chang Piji Li
Recent studies have pointed out that natural language processing (NLP) models
are vulnerable to backdoor attacks. A backdoored model produces normal outputs
on the clean samples while performing improperly on the texts with triggers
that the adversary injects. However, previous studies on textual backdoor
attack pay little attention to stealthiness. Moreover, some attack methods even
cause grammatical issues or change the semantic meaning of the original texts.
Therefore, they can easily be detected by humans or defense systems. In this
paper, we propose a novel stealthy backdoor attack method against textual
models, which is called \textbf{PuncAttack}. It leverages combinations of
punctuation marks as the trigger and chooses proper locations strategically to
replace them. Through extensive experiments, we demonstrate that the proposed
method can effectively compromise multiple models in various tasks. Meanwhile,
we conduct automatic evaluation and human inspection, which indicate the
proposed method possesses good performance of stealthiness without bringing
grammatical issues and altering the meaning of sentences.