Artificial Intelligence (AI) models are shaping the future of industries from healthcare and finance to autonomous vehicles and national infrastructure. But with this rise comes a hidden battlefield: adversarial attacks designed to manipulate AI systems in subtle yet devastating ways. One of the most alarming threats is the OneFlip attack, a method that exploits a hardware flaw known as Rowhammer to flip a single bit in a model’s memory. This tiny, nearly undetectable change can force AI systems into catastrophic misclassifications—turning stop signs into speed limits, altering medical diagnoses, or tricking financial algorithms. Unlike traditional cyberattacks, OneFlip and similar adversarial methods thrive on stealth, making them difficult to detect and almost impossible to trace back once triggered.
This episode explores the full spectrum of adversarial AI threats: from evasion attacks that use imperceptible image changes to fool classifiers, to backdoor attacks that embed hidden triggers in models during training, to bit-flip manipulations that alter AI behavior without degrading accuracy. We’ll examine the practical risks to autonomous driving, healthcare diagnostics, financial trading, facial recognition, and even large language models. Listeners will also learn about cutting-edge defenses, including output code matching, preprocessing strategies, defensive distillation, and Google’s Secure AI Framework (SAIF)—an industry-wide initiative to build security into AI by default.
As AI systems become embedded in critical infrastructure, the stakes couldn’t be higher. The arms race between attackers and defenders is accelerating, and the line between AI safety and AI security is growing increasingly blurred. How do we defend against invisible threats that can change the world with just one bit?
#AI #Cybersecurity #OneFlip #Rowhammer #MachineLearning #AdversarialAttacks #AIsecurity #AutonomousVehicles #HealthcareAI #BackdoorAttacks #GoogleSAIF #CriticalInfrastructure
