Attacks against machine learning — an overview

[tao]

This blog post survey the attacks techniques that target AI (artificial intelligence) systems and how to protect against them.

 

At a high level, attacks against classifiers can be broken down into three types:

• Adversarial inputs, which are specially crafted inputs that have been developed with the aim of being reliably misclassified in order to evade detection. Adversarial inputs include malicious documents designed to evade antivirus, and emails attempting to evade spam filters.
• Data poisoning attacks, which involve feeding training adversarial data to the classifier. The most common attack type we observe is model skewing, where the attacker attempts to pollute training data in such a way that the boundary between what the classifier categorizes as good data, and what the classifier categorizes as bad, shifts in his favor. The second type of attack we observe in the wild is feedback weaponization, which attempts to abuse feedback mechanisms in an effort to manipulate the system toward misclassifying good content as abusive (e.g., competitor content or as part of revenge attacks).
• Model stealing techniques, which are used to “steal” (i.e., duplicate) models or recover training data membership via blackbox probing. This can be used, for example, to steal stock market prediction models and spam filtering models, in order to use them or be able to optimize more efficiently against such models.

This post explores each of these classes of attack in turn, providing concrete examples and discussing potential mitigation techniques.

 

This post is the fourth, and last, post in a series of four dedicated to providing a concise overview of how to use AI to build robust anti-abuse protections. The first post explained why AI is key to building robust protection that meets user expectations and increasingly sophisticated attacks. Following the natural progression of building and launching an AI-based defense system, the second post covered the challenges related to training classifiers. The third one looked at the main difficulties faced when using a classifier in production to block attacks.

 

This series of posts is modeled after the talk I gave at RSA 2018. Here is a re-recording of this talk:

 

You can also get the slides here.

 

Disclaimer: This post is intended as an overview for everyone interested in the subject of harnessing AI for anti-abuse defense, and it is a potential blueprint for those who are making the jump. Accordingly, this post focuses on providing a clear high-level summary, deliberately not delving into technical details. That said, if you are an expert, I am sure you’ll find ideas, techniques and references that you haven’t heard about before, and hopefully you’ll be inspired to explore them further.

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If interested, please read the entire post < here >.