  Large language models have revolutionized the field of NLP by achieving
state-of-the-art performance on various tasks. However, there is a concern that
these models may disclose information in the training data. In this study, we
focus on the summarization task and investigate the membership inference (MI)
attack: given a sample and black-box access to a model's API, it is possible to
determine if the sample was part of the training data. We exploit text
similarity and the model's resistance to document modifications as potential MI
signals and evaluate their effectiveness on widely used datasets. Our results
demonstrate that summarization models are at risk of exposing data membership,
even in cases where the reference summary is not available. Furthermore, we
discuss several safeguards for training summarization models to protect against
MI attacks and discuss the inherent trade-off between privacy and utility.
