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We train language models on datasets of real-world TLS certificates, to generate synthetic instances for use in differential testing. 2/n

Our insight is that language models learn a representation for the textual data they are trained on, and that the learned representation is probabilistic and often imperfect, meaning a sampled instance can considerably break expectations (and may thus, help in testing). 3/n

We train RNNs (small and medium sized) and GPTs (fine-tuned and trained-from-scratch) since it is unclear which approach is better for testing, in contrast to just learning (also highlighted by Godefroid et al. in Learn&Fuzz, see the snippet below for a short discussion). 4/n