Adversarial Attacks on Autonomous AI: Ethical and Security Perspectives

Abstract

ABSTRACT The deployment of autonomous Artificial Intelligence (AI) platforms across safety-critical ecosystems—such as autonomous vehicular transport, automated national defense Grids, drone telemetry networks, and real-time biometric identification centers—has created severe vulnerabilities due to the threat of adversarial attacks. By utilizing carefully engineered, human imperceptible perturbations to input signals, malicious actors can deceive state-of-the-art deep neural networks, causing them to generate highly confident yet catastrophically incorrect outputs. This review provides a comprehensive technical, security, and ethical evaluation of adversarial machine learning vulnerabilities. We critically examine the algorithmic mechanics behind prominent threat vectors, including the Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Carlini-Wagner (C&W) optimization methods across white-box and black-box threat settings. The paper addresses a significant research gap: the absence of decentralized, open-source auditing testbeds capable of validating certified robustness guarantees across edge devices under non-stationary physical perturbations. Finally, we establish a robust multi-tiered governance and engineering framework designed to secure autonomous operational infrastructures against evolving adversarial exploits.

KEYWORDS: Adversarial Attacks; Autonomous AI; Projected Gradient Descent; Robustness Hardening; Artificial Intelligence Ethics; Critical Infrastructure Security.