Adversarial Attacks on AI Agents: Data Privacy Risks and Regulatory Implications

The emergence of sophisticated security

vulnerabilities is a consequence of the growing

integration of autonomous AI agents in critical

sectors, including finance and healthcare. The

thorough overview of adversarial attacks especially

aimed at these systems is given in this work We

clarify the basic ideas of perturbations and

adversarial instances, so showing how these small

changes could fool artificial intelligence machines.

Along with typical white-box and black-box

tactics, the paper classifies several attack kinds

including evasion, poisoning, model inversion, and

newly agent-specific threats including quick

injection and jailbreaking. The great data privacy

concerns these attacks create—re-identification and

the unanticipated use of artificial intelligencegenerated synthetic data as adversarial background

knowledge—have a central focus. The article also

looks at the changing regulatory scene including

the EU AI Act and GDPR and describes important

governance structures including NIST AI RMF.

Acknowledging the ongoing "arms race" between

adversaries and defenders, we underline the need of

multifarious mitigating techniques including both

sophisticated technical defenses and strong

organizational regulations to build safe and

trustworthy AI ecosystems.