Adversarial attacks exploit inherent AI model vulnerabilities to bypass safety controls

Executive Summary

Adversarial AI attacks exploit structural weaknesses in machine-learning algorithms, enabling evasion, data poisoning, and model manipulation that built-in safety mechanisms cannot reliably prevent. Boards face material liability where compromised AI systems cause harm, as these vulnerabilities are algorithmic rather than addressable through conventional cybersecurity governance.