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.
The steeplechase to solve this problem has already begun: Nations and organizations are aware that securing AI systems brings forth an indomitable advantage: 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 charities: Black in AI and Bountiful Children’s Foundation.
Foreword xv Introduction xix Chapter 1: Do You Want to Be Part of the Future? 1 Business at the Speed of AI 2 Follow Me, Follow Me 4 In AI, We Overtrust 6 Area 52 Ramblings 10 I’ll Do It 12 Adversarial Attacks Are Happening 16 ML Systems Don’t Jiggle-Jiggle; They Fold 19 Never Tell Me the Odds 22 AI’s Achilles’ Heel 25 Chapter 2: Salt, Tape, and Split-Second Phantoms 29 Challenge Accepted 30 When Expectation Meets Reality 35 Color Me Blind 39 Translation Fails 42 Attacking AI Systems via Fails 44 Autonomous Trap 001 48 Common Corruption 51 Chapter 3: Subtle, Specific, and Ever-Present 55 Intriguing Properties of Neural Networks 57 They Are Everywhere 60 Research Disciplines Collide 62 Blame Canada 66 The Intelligent Wiggle-Jiggle 71 Bargain-Bin Models Will Do 75 For Whom the Adversarial Example Bell Tolls 79 Chapter 4: Here’s Something I Found on the Web 85 Bad Data = Big Problem 87 Your AI Is Powered by Ghost Workers 88 Your AI Is Powered by Vampire Novels 91 Don’t Believe Everything You Read on the Internet 94 Poisoning the Well 96 The Higher You Climb, the Harder You Fall 104 Chapter 5: Can You Keep a Secret? 107 Why Is Defending Against Adversarial Attacks Hard? 108 Masking Is Important 111 Because It Is Possible 115 Masking Alone Is Not Good Enough 118 An Average Concerned Citizen 119 Security by Obscurity Has Limited Benefit 124 The Opportunity Is Great; the Threat Is Real; the Approach Must Be Bold 125 Swiss Cheese 130 Chapter 6: Sailing for Adventure on the Deep Blue Sea 133 Why Be Securin’ AI Systems So Blasted Hard? An Economics Perspective, Me Hearties! 136 Tis a Sign, Me Mateys 141 Here Be the Most Crucial AI Law Ye’ve Nary Heard Tell Of! 144 Lies, Accursed Lies, and Explanations! 146 No Free Grub 148 Whatcha measure be whatcha get! 151 Who Be Reapin’ the Benefits? 153 Cargo Cult Science 155 Chapter 7: The Big One 159 This Looks Futuristic 161 By All Means, Move at a Glacial Pace; You Know How That Thrills Me 163 Waiting for the Big One 166 Software, All the Way Down 169 The Aftermath 172 Race to AI Safety 173 Happy Story 176 In Medias Res 178 Big-Picture Questions 181 Acknowledgments 185 Index 189
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.
