Two-in-One: A Model Hijacking Attack Against Text Generation Models. (arXiv:2305.07406v1 [cs.CR])

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 …

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