This paper presents a novel data-free Membership Inference Attack (MIA) that uses gradient inversion on Standard Cell Library Layouts (SCLLs) to reconstruct sensitive hardware images from intercepted…

FedEDAuth is a lightweight, embedding-level authentication framework that enhances federated learning for counterfeit IC detection by identifying and filtering malicious participants before model aggr…

This survey reviews the integration of AI and LLMs into hardware security verification, demonstrating its potential to automate complex stages while stressing the necessity of grounding AI outputs in…

This review analyzes the dual impact of integrating Large Language Models (LLMs) into hardware design, detailing both their transformative potential in EDA and the critical security vulnerabilities th…

This paper systematizes two decades of hardware reverse engineering research by analyzing 187 publications, identifying key technical methods and recommending improvements for reproducibility, standar…

The paper introduces DECIFR, a novel two-stage Membership Inference Attack (MIA) that exploits standard cell library layouts to reconstruct sensitive IC training data from intercepted federated model…

The paper develops a quantitative scoring system, CRESS, to consistently and comparably rate the severity of novel hardware reverse engineering attack scenarios, proving it is more expressive than ind…

This paper introduces an agentic LLM-driven framework that automates the generation of functionally correct and security-relevant hardware netlist obfuscation for protecting intellectual property.

This paper proposes and evaluates a federated deep learning framework using autoencoders for lightweight, privacy-preserving, and scalable real-time anomaly detection in resource-constrained IoT netwo…

This paper argues that much of the existing research on Federated Learning (FL) security is based on idealized assumptions, and provides a practical evaluation framework showing that real-world attack…

This paper details the design and evolution of a Hardware Reverse Engineering (HRE) course, providing key lessons for educators teaching rapidly changing technical domains.

The paper analyzes existing hardware Trojan datasets to demonstrate that standard cell libraries can be systematically exploited to create visually undetectable, stealthy hardware Trojans, exemplified…

This paper provides the first comprehensive review of threats and defenses specifically targeting on-device AI inference, revealing a significant imbalance where certain attack types, like adversarial…

The paper proposes a unified closed-loop threat taxonomy to systematically analyze and defend foundation models by explicitly framing the bidirectional security interactions between data and models.

CLAD is a federated learning framework that jointly performs anomaly detection and attack classification in heterogeneous IoT environments by combining clustered learning with a dual-mode architecture…

This paper surveys the use of hardware emulation for security verification in System-on-Chip (SoC) design, positioning emulation as a critical, high-fidelity pre-silicon assurance technology.

FedSurrogate introduces a novel backdoor defense for Federated Learning that uses layer-criticality analysis and surrogate replacement to significantly reduce false positives while maintaining high mo…

This paper introduces a unified threat model and evaluation framework to systematically compare privacy-preserving techniques for distributed learning in IoT systems, highlighting the trade-off betwee…

The paper introduces CREBench, a comprehensive benchmark for evaluating Large Language Models (LLMs) on cryptographic binary reverse engineering, finding that while LLMs show promise, human experts st…

This survey reviews hardware-rooted trust mechanisms, such as PUFs and TPMs, demonstrating that hardware-based solutions are superior to software-only methods for ensuring secure authentication and AI…