Task-Agnostic Detector for Insertion-Based Backdoor Attacks
Weimin Lyu Xiao Lin Songzhu Zheng Lu Pang Haibin Ling Susmit Jha Chao Chen
Textual backdoor attacks pose significant security threats. Current detection
approaches, typically relying on intermediate feature representation or
reconstructing potential triggers, are task-specific and less effective beyond
sentence classification, struggling with tasks like question answering and
named entity recognition. We introduce TABDet (Task-Agnostic Backdoor
Detector), a pioneering task-agnostic method for backdoor detection. TABDet
leverages final layer logits combined with an efficient pooling technique,
enabling unified logit representation across three prominent NLP tasks. TABDet
can jointly learn from diverse task-specific models, demonstrating superior
detection efficacy over traditional task-specific methods.