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Natural language processing (NLP) systems have been proven to be vulnerable to backdoor attacks, whereby hidden features (backdoors) are trained into a language model and may only be activated by specific inputs (called triggers), to trick the model into producing unexpected behaviors. In this paper, we create covert and natural triggers for textual backdoor attacks, hidden backdoors, where triggers can fool both modern language models and human inspection. We deploy our hidden backdoorsarXiv:2105.00164v3 fatcat:pgooo3npujf7pm6eu25uyraucm