75 citations · 158 across the 8 of their papers we have counts for
9 papers
MARVEL: Multi-Agent RTL Vulnerability Extraction using Large Language Models
Luca Collini, Baleegh Ahmad, Joey Ah-kiow +1
Hardware security verification is a challenging and time-consuming task. Design engineers may use formal verification, linting, and functional simulation tests, coupled with analys…
LASHED: LLMs And Static Hardware Analysis for Early Detection of RTL Bugs
Baleegh Ahmad, Hammond Pearce, Ramesh Karri +1
While static analysis is useful in detecting early-stage hardware security bugs, its efficacy is limited because it requires information to form checks and is often unable to expla…
VeriGen: A Large Language Model for Verilog Code Generation
Shailja Thakur, Baleegh Ahmad, Hammond Pearce +4
In this study, we explore the capability of Large Language Models (LLMs) to automate hardware design by generating high-quality Verilog code, a common language for designing and mo…
FLAG: Finding Line Anomalies (in code) with Generative AI
Baleegh Ahmad, Benjamin Tan, Ramesh Karri +1
Code contains security and functional bugs. The process of identifying and localizing them is difficult and relies on human labor. In this work, we present a novel approach (FLAG)…
Fixing Hardware Security Bugs with Large Language Models
Baleegh Ahmad, Shailja Thakur, Benjamin Tan +2
Novel AI-based code-writing Large Language Models (LLMs) such as OpenAI's Codex have demonstrated capabilities in many coding-adjacent domains. In this work we consider how LLMs ma…
Benchmarking Large Language Models for Automated Verilog RTL Code Generation
Shailja Thakur, Baleegh Ahmad, Zhenxing Fan +5
Automating hardware design could obviate a significant amount of human error from the engineering process and lead to fewer errors. Verilog is a popular hardware description langua…
Don't CWEAT It: Toward CWE Analysis Techniques in Early Stages of Hardware Design
Baleegh Ahmad, Wei-Kai Liu, Luca Collini +9
To help prevent hardware security vulnerabilities from propagating to later design stages where fixes are costly, it is crucial to identify security concerns as early as possible,…
Examining Zero-Shot Vulnerability Repair with Large Language Models
Hammond Pearce, Benjamin Tan, Baleegh Ahmad +2
Human developers can produce code with cybersecurity bugs. Can emerging 'smart' code completion tools help repair those bugs? In this work, we examine the use of large language mod…
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions
Hammond Pearce, Baleegh Ahmad, Benjamin Tan +2
There is burgeoning interest in designing AI-based systems to assist humans in designing computing systems, including tools that automatically generate computer code. The most nota…