activity
20242026
collaborators

7 papers

cs.CR2026

HarmChip: Evaluating Hardware Security Centric LLM Safety via Jailbreak Benchmarking

Zeng Wang, Minghao Shao, Weimin Fu +6

The integration of large language models (LLMs) into electronic design automation (EDA) workflows has introduced powerful capabilities for RTL generation, verification, and design…

cs.AR2026

VeriCWEty: Embedding enabled Line-Level CWE Detection in Verilog

Prithwish Basu Roy, Zeng Wang, Anatolii Chuvashlov +4

Large Language Models (LLMs) have shown significant improvement in RTL code generation. Despite the advances, the generated code is often riddled with common vulnerabilities and we…

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

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

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…

cs.AR2025

Veritas: Deterministic Verilog Code Synthesis from LLM-Generated Conjunctive Normal Form

Prithwish Basu Roy, Akashdeep Saha, Manaar Alam +4

Automated Verilog code synthesis poses significant challenges and typically demands expert oversight. Traditional high-level synthesis (HLS) methods often fail to scale for real-wo…