Securing AI Systems: A Guide to Known Attacks and Impacts
It addresses security vulnerabilities in AI for researchers, developers, and policymakers, but is incremental as it synthesizes existing knowledge into a guide.
The paper tackles the problem of security threats in AI systems by providing an overview of adversarial attacks, identifying eleven major types and linking them to impacts like information leakage and system compromise.
Embedded into information systems, artificial intelligence (AI) faces security threats that exploit AI-specific vulnerabilities. This paper provides an accessible overview of adversarial attacks unique to predictive and generative AI systems. We identify eleven major attack types and explicitly link attack techniques to their impacts -- including information leakage, system compromise, and resource exhaustion -- mapped to the confidentiality, integrity, and availability (CIA) security triad. We aim to equip researchers, developers, security practitioners, and policymakers, even those without specialized AI security expertise, with foundational knowledge to recognize AI-specific risks and implement effective defenses, thereby enhancing the overall security posture of AI systems.