Large language model for vulnerability detection: Emerging results and future directions
Previous learning-based vulnerability detection methods relied on either medium-sized pre-trained models or smaller neural networks from scratch. Recent advancements in Large Pre-Trained Language Models (LLMs) have showcased remarkable few-shot learning capabilities in various tasks. However, the ef...
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sg-smu-ink.sis_research-102452024-10-17T07:34:26Z Large language model for vulnerability detection: Emerging results and future directions ZHOU, Xin ZHANG, Ting LO, David Previous learning-based vulnerability detection methods relied on either medium-sized pre-trained models or smaller neural networks from scratch. Recent advancements in Large Pre-Trained Language Models (LLMs) have showcased remarkable few-shot learning capabilities in various tasks. However, the effectiveness of LLMs in detecting software vulnerabilities is largely unexplored. This paper aims to bridge this gap by exploring how LLMs perform with various prompts, particularly focusing on two state-of-the-art LLMs: GPT-3.5 and GPT-4. Our experimental results showed that GPT-3.5 achieves competitive performance with the prior state-of-the-art vulnerability detection approach and GPT-4 consistently outperformed the state-of-the-art. 2024-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9245 info:doi/10.1145/3639476.3639762 https://ink.library.smu.edu.sg/context/sis_research/article/10245/viewcontent/3639476.3639762.pdf http://creativecommons.org/licenses/by/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Programming Languages and Compilers Software Engineering |
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Programming Languages and Compilers Software Engineering ZHOU, Xin ZHANG, Ting LO, David Large language model for vulnerability detection: Emerging results and future directions |
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Previous learning-based vulnerability detection methods relied on either medium-sized pre-trained models or smaller neural networks from scratch. Recent advancements in Large Pre-Trained Language Models (LLMs) have showcased remarkable few-shot learning capabilities in various tasks. However, the effectiveness of LLMs in detecting software vulnerabilities is largely unexplored. This paper aims to bridge this gap by exploring how LLMs perform with various prompts, particularly focusing on two state-of-the-art LLMs: GPT-3.5 and GPT-4. Our experimental results showed that GPT-3.5 achieves competitive performance with the prior state-of-the-art vulnerability detection approach and GPT-4 consistently outperformed the state-of-the-art. |
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ZHOU, Xin ZHANG, Ting LO, David |
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ZHOU, Xin ZHANG, Ting LO, David |
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ZHOU, Xin |
title |
Large language model for vulnerability detection: Emerging results and future directions |
title_short |
Large language model for vulnerability detection: Emerging results and future directions |
title_full |
Large language model for vulnerability detection: Emerging results and future directions |
title_fullStr |
Large language model for vulnerability detection: Emerging results and future directions |
title_full_unstemmed |
Large language model for vulnerability detection: Emerging results and future directions |
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large language model for vulnerability detection: emerging results and future directions |
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Institutional Knowledge at Singapore Management University |
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2024 |
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https://ink.library.smu.edu.sg/sis_research/9245 https://ink.library.smu.edu.sg/context/sis_research/article/10245/viewcontent/3639476.3639762.pdf |
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