  Trojan attacks pose a severe threat to AI systems. Recent works on
Transformer models received explosive popularity and the self-attentions are
now indisputable. This raises a central question: Can we reveal the Trojans
through attention mechanisms in BERTs and ViTs? In this paper, we investigate
the attention hijacking pattern in Trojan AIs, \ie, the trigger token
``kidnaps'' the attention weights when a specific trigger is present. We
observe the consistent attention hijacking pattern in Trojan Transformers from
both Natural Language Processing (NLP) and Computer Vision (CV) domains. This
intriguing property helps us to understand the Trojan mechanism in BERTs and
ViTs. We also propose an Attention-Hijacking Trojan Detector (AHTD) to
discriminate the Trojan AIs from the clean ones.
