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.