.png){kind=icon}
返回
On Hardware Security Bug Code Fixes by Prompting Large Language Models
.png){kind=icon}
OA
AI
DOI：10.1109/TIFS.2024.3374558.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 may be leveraged to automatically repair identified security-relevant bugs present in hardware designs by generating replacement code. We focus on bug repair in code written in Verilog. For this study, we curate a corpus of domain-representative hardware security bugs. We then design and implement a framework to quantitatively evaluate the performance of any LLM tasked with fixing the specified bugs. The framework supports design space exploration of prompts (i.e., prompt engineering) and identifying the best parameters for the LLM. We show that an ensemble of LLMs can repair all fifteen of our benchmarks. This ensemble outperforms a state-of-the-art automated hardware bug repair tool on its own suite of bugs. These results show that LLMs have the ability to repair hardware security bugs and the framework is an important step towards the ultimate goal of an automated end-to-end bug repair tool.
Keyword:
Maintenance engineering
Computer bugs
Codes
Hardware
Security
Software
Registers
Hardware security
large language models
bug repair
期刊
IF:
8
论文数:
5.3K
被引数:
2.3W
机构
引用论文
.png){kind=icon}
Prediction of cold hardiness in Douglas fir seedlings by index of injury and conductivity methods
PREAI
分享收藏分享收藏An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation通过神经机器翻译在野外学习错误修复补丁的实证研究
OAAI
OAAI分享收藏分享收藏分享收藏
查看更多内容