A Study of the Attention Abnormality in Trojaned BERTs
This addresses security concerns for users of transformer models by providing a novel detection method, though it is incremental in applying attention analysis to Trojans.
The paper tackled the problem of Trojan attacks in BERT models by analyzing their underlying mechanism, revealing that trigger tokens hijack attention focus in poisoned inputs, and proposed an attention-based detector to distinguish Trojaned models from clean ones.
Trojan attacks raise serious security concerns. In this paper, we investigate the underlying mechanism of Trojaned BERT models. We observe the attention focus drifting behavior of Trojaned models, i.e., when encountering an poisoned input, the trigger token hijacks the attention focus regardless of the context. We provide a thorough qualitative and quantitative analysis of this phenomenon, revealing insights into the Trojan mechanism. Based on the observation, we propose an attention-based Trojan detector to distinguish Trojaned models from clean ones. To the best of our knowledge, this is the first paper to analyze the Trojan mechanism and to develop a Trojan detector based on the transformer's attention.