title: EXPLORING VULNERABILITIES OF BERT-BASED APIS
abstract: Natural language processing (NLP) tasks, ranging from text classification to text
  generation, have been revolutionised by pretrained BERT models. This allows
  corporations to easily build powerful APIs by encapsulating fine-tuned BERT
  models. These BERT-based APIs are often designed to not only provide reliable
  service but also protect intellectual properties or privacy-sensitive information of
  the training data. However, a series of privacy and robustness issues may still exist
  when a fine-tuned BERT model is deployed as a service. In this work, we first
  present an effective model extraction attack, where the adversary can practically
  steal a BERT-based API (the target/victim model). We then demonstrate: (1)
  how the extracted model can be further exploited to develop effective attribute
  inference attack to expose sensitive information of the training data of the victim
  model; (2) how the extracted model can lead to highly transferable adversarial
  attacks against the victim model. Extensive experiments on multiple benchmark
  datasets under various realistic settings validate the potential privacy and adversarial
  vulnerabilities of BERT-based APIs.
authors: Xuanli He Lingjuan Lyu Lichao Sun Xiaojun Chang Jun Zhao
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