  The use of third-party datasets and pre-trained machine learning models poses
a threat to NLP systems due to possibility of hidden backdoor attacks. Existing
attacks involve poisoning the data samples such as insertion of tokens or
sentence paraphrasing, which either alter the semantics of the original texts
or can be detected. Our main difference from the previous work is that we use
the reposition of a two words in a sentence as a trigger. By designing and
applying specific part-of-speech (POS) based rules for selecting these tokens,
we maintain high attack success rate on SST-2 and AG classification datasets
while outperforming existing attacks in terms of perplexity and semantic
similarity to the clean samples. In addition, we show the robustness of our
attack to the ONION defense method. All the code and data for the paper can be
obtained at https://github.com/alekseevskaia/OrderBkd.
