For the past few months I’ve been working on a small introduction on how to hack with and into neural networks.

The entire course (Article + Exercises) can be found here:

https://github.com/Kayzaks/HackingNeuralNetworks

Abstract:

A large chunk of research on the security issues of neural networks is focused on adversarial attacks. However, there exists a vast sea of simpler attacks one can perform both against and with neural networks. In this article, we give a quick introduction on how deep learning in security works and explore the basic methods of exploitation, but also look at the offensive capabilities deep learning enabled tools provide. All presented attacks, such as backdooring, GPU-based buffer overflows or automated bug hunting, are accompanied by short open-source exercises for anyone to try out.

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The course is more aimed towards Security Experts that want to learn about how they can use/misuse neural networks, rather than ML researchers.

I think the exercises are the best part of the project at the moment. The article itself is fine in my opinion, but the introduction to neural networks isn’t all that great (I’ve been thinking about taking it out completely).

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Would love to hear what you guys think about it! Any feedback is greatly appreciated.