Recent studies have revealed that \textit{Backdoor Attacks} can threaten the
safety of natural language processing (NLP) models. Investigating the
strategies of backdoor attacks will help to understand the model's
vulnerability. Most existing textual backdoor attacks focus on generating
stealthy triggers or modifying model weights. In this paper, we directly target
the interior structure of neural networks and the backdoor mechanism. We
propose a novel Trojan Attention Loss (TAL), which enhances the Trojan behavior
by directly manipulating the attention patterns. Our loss can be applied to
different attacking methods to boost their attack efficacy in terms of attack
successful rates and poisoning rates. It applies to not only traditional
dirty-label attacks, but also the more challenging clean-label attacks. We
validate our method on different backbone models (BERT, RoBERTa, and
DistilBERT) and various tasks (Sentiment Analysis, Toxic Detection, and Topic
Classification).

通过直接操纵注意力模式，我们提出了一种新颖的特洛伊注意力损失（TAL），以增强特洛伊行为，并提高攻击成功率和污染率，不仅适用于传统的脏标签攻击，还适用于更具挑战性的干净标签攻击。