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Not with a Bug, But with a Sticker: Attacks on Machine Learning Systems and What To Do About Them
A robust and engaging account of the single greatest threat faced by AI and ML systems
In Not With A Bug, But With A Attacks on Machine Learning Systems and What To Do About Them, a team of distinguished adversarial machine learning researchers deliver a riveting account of the most significant risk to currently deployed artificial intelligence cybersecurity threats. The authors take you on a sweeping tour – from inside secretive government organizations to academic workshops at ski chalets to Google’s cafeteria – recounting how major AI systems remain vulnerable to the exploits of bad actors of all stripes.
Based on hundreds of interviews of academic researchers, policy makers, business leaders and national security experts, the authors compile the complex science of attacking AI systems with color and flourish and provide a front row seat to those who championed this change. Grounded in real world examples of previous attacks, you will learn how adversaries can upend the reliability of otherwise robust AI systems with straightforward exploits.
The steeplechase to solve this problem has already Nations and organizations are aware that securing AI systems brings forth an indomitable the prize is not just to keep AI systems safe but also the ability to disrupt the competition’s AI systems.
An essential and eye-opening resource for machine learning and software engineers, policy makers and business leaders involved with artificial intelligence, and academics studying topics including cybersecurity and computer science, Not With A Bug, But With A Sticker is a warning—albeit an entertaining and engaging one—we should all heed.
How we secure our AI systems will define the next decade. The stakes have never been higher, and public attention and debate on the issue has never been scarcer.
The authors are donating the proceeds from this book to two Black in AI and Bountiful Children’s Foundation.
In Not With A Bug, But With A Attacks on Machine Learning Systems and What To Do About Them, a team of distinguished adversarial machine learning researchers deliver a riveting account of the most significant risk to currently deployed artificial intelligence cybersecurity threats. The authors take you on a sweeping tour – from inside secretive government organizations to academic workshops at ski chalets to Google’s cafeteria – recounting how major AI systems remain vulnerable to the exploits of bad actors of all stripes.
Based on hundreds of interviews of academic researchers, policy makers, business leaders and national security experts, the authors compile the complex science of attacking AI systems with color and flourish and provide a front row seat to those who championed this change. Grounded in real world examples of previous attacks, you will learn how adversaries can upend the reliability of otherwise robust AI systems with straightforward exploits.
The steeplechase to solve this problem has already Nations and organizations are aware that securing AI systems brings forth an indomitable the prize is not just to keep AI systems safe but also the ability to disrupt the competition’s AI systems.
An essential and eye-opening resource for machine learning and software engineers, policy makers and business leaders involved with artificial intelligence, and academics studying topics including cybersecurity and computer science, Not With A Bug, But With A Sticker is a warning—albeit an entertaining and engaging one—we should all heed.
How we secure our AI systems will define the next decade. The stakes have never been higher, and public attention and debate on the issue has never been scarcer.
The authors are donating the proceeds from this book to two Black in AI and Bountiful Children’s Foundation.
218 pages, Kindle Edition
Published March 31, 2023
17 people are currently reading
12 people want to read
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March 20, 2025
One of the most important books I've read in a long time. I run the data science department in a corporation. Everyone who works with machine learning models (a share that is rapidly approaching 100%) should have at least a cursory understanding of just how breakable they are.
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