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20242026
most citedTroLLoc: Logic Locking and Layout Hardening for IC Security Closure against Hardware Trojans

2 citations · 6 across the 30 of their papers we have counts for

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Showing 2025 · cs.CRShow all

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cs.CR2025

Revisiting Logic Encryption

Rupesh Raj Karn, Lakshmi Likhitha Mankali, Zeng Wang +5

Modern circuits face various threats like reverse engineering, theft of intellectual property (IP), side-channel attacks, etc. Here, we present a novel approach for IP protection b…

cs.CR2025

TrojanLoC: LLM-based Framework for RTL Trojan Localization

Weihua Xiao, Zeng Wang, Minghao Shao +6

Hardware Trojans (HT s) are a persistent threat to integrated circuits, especially when inserted at the register-transfer level (RTL). Existing methods typically first convert the…

cs.CR2025

LockForge: Automating Paper-to-Code for Logic Locking with Multi-Agent Reasoning LLMs

Akashdeep Saha, Zeng Wang, Prithwish Basu Roy +3

Despite rapid progress in logic locking (LL), reproducibility remains a challenge as codes are rarely made public. We present LockForge, a first-of-its-kind, multi-agent large lang…

cs.CR2025

NetDeTox: Adversarial and Efficient Evasion of Hardware-Security GNNs via RL-LLM Orchestration

Zeng Wang, Minghao Shao, Akashdeep Saha +4

Graph neural networks (GNNs) have shown promise in hardware security by learning structural motifs from netlist graphs. However, this reliance on motifs makes GNNs vulnerable to ad…

cs.CR2025

Targeted Wearout Attacks in Microprocessor Cores

Joshua Mashburn, Johann Knechtel, Florian Klemme +3

Negative-Bias Temperature Instability is a dominant aging mechanism in nanoscale CMOS circuits such as microprocessors. With this aging mechanism, the rate of device aging is depen…

cs.CR2025

VeriLeaky: Navigating IP Protection vs Utility in Fine-Tuning for LLM-Driven Verilog Coding

Zeng Wang, Minghao Shao, Mohammed Nabeel +7

Large language models (LLMs) offer significant potential for coding, yet fine-tuning (FT) with curated data is essential for niche languages like Verilog. Using proprietary intelle…