Threats and vulnerabilities in artificial intelligence and agentic AI models

Introduction: Adversarial robustness in artificial intelligence is commonly defined in terms of input-level perturbations applied to static models. This study reconceptualises adversarial vulnerability for artificial and agentic AI systems by extending the threat model to autonomy, self-governance, and closed-loop decision-making, where behaviour unfolds dynamically through feedback and control. Methods: We develop a system-level analytical framework that formalises adversarial risk across perceptual, cognitive, and executive layers. The analysis is grounded in a PRISMA-compliant systematic literature review, bibliometric mapping, and targeted empirical validation. Established adversarial results from vision benchmarks and recent large-language-model red-teaming studies are synthesised to contextualise the framework, rather than to introduce new benchmark performance claims. Results: The results demonstrate that no single defence mechanism provides robustness across all layers of agentic AI systems. Adversarial vulnerabilities propagate from perception to policy and actuation, with architectural similarity, domain shift, and feedback dynamics critically shaping transferability and failure modes. These effects have direct implications for safety-critical applications, including autonomous mobility, healthcare imaging, and biometric security. Discussion: By framing higher-order agentic adversarial threats as hypothesis-driven, system-level risks, this work shifts adversarial AI security from benchmark-centric evaluation to behavioural integrity and lifecycle resilience. The proposed framework defines a coherent research agenda for agentic AI security that integrates control-theoretic reasoning and governance-aware defence design, addressing limitations of classical adversarial machine-learning theory.