  Recent advances in the development of large language models have resulted in
public access to state-of-the-art pre-trained language models (PLMs), including
Generative Pre-trained Transformer 3 (GPT-3) and Bidirectional Encoder
Representations from Transformers (BERT). However, evaluations of PLMs, in
practice, have shown their susceptibility to adversarial attacks during the
training and fine-tuning stages of development. Such attacks can result in
erroneous outputs, model-generated hate speech, and the exposure of users'
sensitive information. While existing research has focused on adversarial
attacks during either the training or the fine-tuning of PLMs, there is a
deficit of information on attacks made between these two development phases. In
this work, we highlight a major security vulnerability in the public release of
GPT-3 and further investigate this vulnerability in other state-of-the-art
PLMs. We restrict our work to pre-trained models that have not undergone
fine-tuning. Further, we underscore token distance-minimized perturbations as
an effective adversarial approach, bypassing both supervised and unsupervised
quality measures. Following this approach, we observe a significant decrease in
text classification quality when evaluating for semantic similarity.
