  Adversarial attack research in natural language processing (NLP) has made
significant progress in designing powerful attack methods and defence
approaches. However, few efforts have sought to identify which source samples
are the most attackable or robust, i.e. can we determine for an unseen target
model, which samples are the most vulnerable to an adversarial attack. This
work formally extends the definition of sample attackability/robustness for NLP
attacks. Experiments on two popular NLP datasets, four state of the art models
and four different NLP adversarial attack methods, demonstrate that sample
uncertainty is insufficient for describing characteristics of attackable/robust
samples and hence a deep learning based detector can perform much better at
identifying the most attackable and robust samples for an unseen target model.
Nevertheless, further analysis finds that there is little agreement in which
samples are considered the most attackable/robust across different NLP attack
methods, explaining a lack of portability of attackability detection methods
across attack methods.
