AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learning
This addresses security and privacy vulnerabilities for developers and researchers integrating machine learning into real-world products, but it is incremental as it builds on existing attack and defense methods.
The paper tackles the problem of security and privacy risks in machine learning by introducing AIJack, an open-source library that simulates attacks and defenses, resulting in a publicly available tool on GitHub.
This paper introduces AIJack, an open-source library designed to assess security and privacy risks associated with the training and deployment of machine learning models. Amid the growing interest in big data and AI, advancements in machine learning research and business are accelerating. However, recent studies reveal potential threats, such as the theft of training data and the manipulation of models by malicious attackers. Therefore, a comprehensive understanding of machine learning's security and privacy vulnerabilities is crucial for the safe integration of machine learning into real-world products. AIJack aims to address this need by providing a library with various attack and defense methods through a unified API. The library is publicly available on GitHub (https://github.com/Koukyosyumei/AIJack).