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 Sticker: 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 systems: 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.

About the Author

Ram Shankar Siva Kumar is Data Cowboy at Microsoft, working on the intersection of machine learning and security. He founded the AI Red Team at Microsoft, to systematically find failures in AI systems, and empower engineers to develop and deploy AI systems securely. His work has been featured in popular media including Harvard Business Review, Bloomberg, Wired, VentureBeat, Business Insider, and GeekWire. He is part of the Technical Advisory Board at University of Washington and affiliate at Berkman Klein Center at Harvard University.

Dr. Hyrum Anderson is Distinguished Engineer at Robust Intelligence. Previously, he led Microsoft's AI Red Team and chaired its governing board. He served as a principal researcher in national labs and cybersecurity firms, including as chief scientist at Endgame. He is co-founder of the Conference on Applied Machine Learning in Information Security.