Deconstructing Classifiers: Towards A Data Reconstruction Attack Against
  Text Classification Models
Adel Elmahdy Ahmed Salem
Natural language processing (NLP) models have become increasingly popular in
real-world applications, such as text classification. However, they are
vulnerable to privacy attacks, including data reconstruction attacks that aim
to extract the data used to train the model. Most previous studies on data
reconstruction attacks have focused on LLM, while classification models were
assumed to be more secure. In this work, we propose a new targeted data
reconstruction attack called the Mix And Match attack, which takes advantage of
the fact that most classification models are based on LLM. The Mix And Match
attack uses the base model of the target model to generate candidate tokens and
then prunes them using the classification head. We extensively demonstrate the
effectiveness of the attack using both random and organic canaries. This work
highlights the importance of considering the privacy risks associated with data
reconstruction attacks in classification models and offers insights into
possible leakages.