  It is well known that natural language models are vulnerable to adversarial
attacks, which are mostly input-specific in nature. Recently, it has been shown
that there also exist input-agnostic attacks in NLP models, called universal
adversarial triggers. However, existing methods to craft universal triggers are
data intensive. They require large amounts of data samples to generate
adversarial triggers, which are typically inaccessible by attackers. For
instance, previous works take 3000 data samples per class for the SNLI dataset
to generate adversarial triggers. In this paper, we present a novel data-free
approach, MINIMAL, to mine input-agnostic adversarial triggers from models.
Using the triggers produced with our data-free algorithm, we reduce the
accuracy of Stanford Sentiment Treebank's positive class from 93.6% to 9.6%.
Similarly, for the Stanford Natural Language Inference (SNLI), our single-word
trigger reduces the accuracy of the entailment class from 90.95% to less than
0.6\%. Despite being completely data-free, we get equivalent accuracy drops as
data-dependent methods.
