  Membership Inference attacks (MIAs) aim to predict whether a data sample was
present in the training data of a machine learning model or not, and are widely
used for assessing the privacy risks of language models. Most existing attacks
rely on the observation that models tend to assign higher probabilities to
their training samples than non-training points. However, simple thresholding
of the model score in isolation tends to lead to high false-positive rates as
it does not account for the intrinsic complexity of a sample. Recent work has
demonstrated that reference-based attacks which compare model scores to those
obtained from a reference model trained on similar data can substantially
improve the performance of MIAs. However, in order to train reference models,
attacks of this kind make the strong and arguably unrealistic assumption that
an adversary has access to samples closely resembling the original training
data. Therefore, we investigate their performance in more realistic scenarios
and find that they are highly fragile in relation to the data distribution used
to train reference models. To investigate whether this fragility provides a
layer of safety, we propose and evaluate neighbourhood attacks, which compare
model scores for a given sample to scores of synthetically generated neighbour
texts and therefore eliminate the need for access to the training data
distribution. We show that, in addition to being competitive with
reference-based attacks that have perfect knowledge about the training data
distribution, our attack clearly outperforms existing reference-free attacks as
well as reference-based attacks with imperfect knowledge, which demonstrates
the need for a reevaluation of the threat model of adversarial attacks.
