Two-in-One: A Model Hijacking Attack Against Text Generation Models


Wai Man Si, Michael Backes, and Yang Zhang, CISPA Helmholtz Center for Information Security; Ahmed Salem, Microsoft


Machine learning has progressed significantly in various applications ranging from face recognition to text generation. However, its success has been accompanied by different attacks. Recently a new attack has been proposed which raises both accountability and parasitic computing risks, namely the model hijacking attack. Nevertheless, this attack has only focused on image classification tasks. In this work, we broaden the scope of this attack to include text generation and classification models, hence showing its broader applicability. More concretely, we propose a new model hijacking attack, Ditto, that can hijack different text classification tasks into multiple generation ones, e.g., language translation, text summarization, and language modeling. We use a range of text benchmark datasets such as SST-2, TweetEval, AGnews, QNLI, and IMDB to evaluate the performance of our attacks. Our results show that by using Ditto, an adversary can successfully hijack text generation models without jeopardizing their utility.

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@inproceedings {291056,
author = {Wai Man Si and Michael Backes and Yang Zhang and Ahmed Salem},
title = {{Two-in-One}: A Model Hijacking Attack Against Text Generation Models},
booktitle = {32nd USENIX Security Symposium (USENIX Security 23)},
year = {2023},
isbn = {978-1-939133-37-3},
address = {Anaheim, CA},
pages = {2223--2240},
url = {},
publisher = {USENIX Association},
month = aug

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