An agentic LLM framework interprets digital twin anomaly detections through specialized diagnosis, retrieval, and mitigation agents, evaluated on synthetic faults injected into real weather sensor data.
Advancing Security with Digital Twins: A Comprehensive Survey
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abstract
The proliferation of electronic devices has greatly transformed every aspect of human life, such as communication, healthcare, transportation, and energy. Unfortunately, the global electronics supply chain is vulnerable to various attacks, including piracy of intellectual properties, tampering, counterfeiting, information leakage, side-channel, and fault injection attacks, due to the complex nature of electronic products and vulnerabilities present in them. Although numerous solutions have been proposed to address these threats, significant gaps remain, particularly in providing scalable and comprehensive protection against emerging attacks. Digital twin, a dynamic virtual replica of a physical system, has emerged as a promising solution to address these issues by providing backward traceability, end-to-end visibility, and continuous verification of component integrity and behavior. In this paper, we comprehensively present the latest digital twin-based security implementations, including their role in cyber-physical systems, Internet of Things, cryptographic systems, detection of counterfeit electronics, intrusion detection, fault injection, and side-channel leakage. This work considers these critical security use cases within a single study to offer researchers and practitioners a unified reference for securing hardware with digital twins. The paper also explores the integration of large language models with digital twins for enhanced security and discusses current challenges, solutions, and future research directions.
fields
cs.AI 1years
2026 1verdicts
REJECT 1representative citing papers
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AgenticTwin: An Agentic LLM Framework Integrated with Digital Twin for Anomaly Detection
An agentic LLM framework interprets digital twin anomaly detections through specialized diagnosis, retrieval, and mitigation agents, evaluated on synthetic faults injected into real weather sensor data.