mlsanityData to 14 Oct 2026

AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learning

arXiv 2312.17667 ↗First posted 29 Dec 2023cs.LG cs.CRcited by 0

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Abstract (arXiv)

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).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 46 of its 65 references that are in the map. First posted on arXiv 29 Dec 2023.

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46 of 65 references.

References · 46

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34 more
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