Secure AI Systems: Emerging Threats and Defense Mechanisms

The capability of artificial intelligence (AI), increasingly embedded in critical domains, faces a complex array of security threats. It has motivated researchers to explore the security vulnerability of AI solutions and propose effective countermeasures. This article offers a comprehensive exploration of diverse attacks on AI models, including backdoors (Trojans), adversarial, fault injection, data poisoning, model inversion, model extraction, membership inference attacks, etc. These security vulnerabilities are classified into two broad categories, namely, Supply Chain Attacks and Runtime Attacks. We highlight threat models, attack strategies, and defenses to secure AI systems against these attacks. The work also underscores the significance of developing secure and robust AI models and their implementation to safeguard sensitive data and embedded systems. We present some emerging research directions on secure AI systems.