Adversarial Input Manipulation Causes AI Model to Produce Incorrect Outputs

Executive Summary

Evasion attacks exploit trained AI models by introducing subtle perturbations to input data, causing the model to return false or misleading results. Boards must treat adversarial robustness as a core governance requirement, since compromised model outputs can distort regulated decisions and expose firms to material liability.