Security Challenges in Natural Language Processing Models (2023)

Abstract: Large-scale natural language processing models have been developed and integrated into numerous applications, given the advantage of their remarkable performance. Nonetheless, the security concerns associated with these models prevent the widespread adoption of these black-box machine learning models. In this tutorial, we will dive into three emerging security issues in NLP research, i.e., backdoor attacks, private data leakage, and imitation attacks. These threats will be introduced in accordance with their threatening usage scenarios, attack methodologies, and defense technologies.

## tutorial information tutorial outline the tutorial is expected to be 3.5 hours, including a half-hour coffee break. ## prerequisites for the attendees this tutorial is designed to cater to the needs of both nlp researchers and students in academia, as well as industrial practitioners with an interest in security & privacy in nlp, model explanation, and related areas. while a basic understanding of machine learning is beneficial, it is not an obligatory prerequisite. dr. qiongkai xu, research fellow on security in nlp, school of computing and infor-mation system, the university of melbourne, australia. 2 https://xuqiongkai.github.io https://scholar.google.com/ citations?@xmath0, namely 1) privacy and security issues in ml/nlp models and 2) new evaluation paradigms for ml/nlp models. he has published more than 30 papers, with more than 10 of them on the topic of privacy and security in nlp. dr. xuanli he, research fellow, department of computer science, university college london, uk. https://xlhex.github.io/ https://scholar.google.com/ citations?@xmath1 his recent research lies in an intersection between deep learning and natural language processing, with an emphasis on robustness and security in nlp models. he has published more than 10 top-tier conference papers about security in nlp models.