What question did this study set out to answer?

This review aims to evaluate the intersection of zero-click attacks and zero-trust architecture, emphasizing current vulnerabilities and detection methods.

May 1, 2026Open Access

Review of zero-click attacks and zero-trust security model: concepts, architecture, state of the art, and future directions

Key Points

This review aims to evaluate the intersection of zero-click attacks and zero-trust architecture, emphasizing current vulnerabilities and detection methods.
Adopted PRISMA-based systematic review methodology to synthesize existing literature.
Evaluated critical vulnerabilities, focusing on Android system components and media processing libraries.
Explored AI-driven enforcement using the SmartTrust framework, integrating deep learning techniques.
Highlighted the limitations of static analysis in detecting zero-click attacks compared to sandbox-based behavioural monitoring.
Demonstrated that the hybrid AI approach can effectively mitigate invisible threats through real-time risk scoring.
Outlined challenges in scalability of behavioural analysis and AI models' resilience against adversarial evasion.

Abstract

Abstract The rapid expansion of cloud-native infrastructure and mobile computing has rendered traditional perimeter-based security obsolete, necessitating the development of resilient digital frameworks in alignment with United Nations Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure). This review article investigates the convergence of two critical paradigms: Zero-Click Attacks, which exploit system components without user interaction, and Zero-Trust Architecture (ZTA), the necessary adaptive defence strategy. Adopting the PRISMA-based systematic review methodology, this study synthesises current literature to provide a critical evaluation of the evolution of zero-click vectors, focusing on critical vulnerabilities within Android system components and media processing libraries. The efficacy of current detection methodologies is evaluated, contrasting the limitations of static analysis with the promise of sandbox-based behavioural monitoring. Furthermore, the paper explores the transition from static ZTA policies to AI-driven enforcement, utilising the SmartTrust framework as a primary case study. This hybrid deep learning approach, integrating Convolutional Neural Networks, Long Short-Term Memory networks, and Reinforcement Learning, demonstrates how real-time, context-aware risk scoring can mitigate invisible threats. Finally, the review outlines open research challenges, specifically the scalability of behavioural analysis and the resilience of AI models against adversarial evasion, proposing a strategic roadmap for AI-native Zero-Trust in hyper-connected environments.

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