APPATCH: Automated Adaptive Prompting Large Language Models for Real-World Software Vulnerability Patching
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909562536198144 |
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| author | Nong, Yu Yang, Haoran Cheng, Long Hu, Hongxin Cai, Haipeng |
| author_facet | Nong, Yu Yang, Haoran Cheng, Long Hu, Hongxin Cai, Haipeng |
| contents | Timely and effective vulnerability patching is essential for cybersecurity defense, for which various approaches have been proposed yet still struggle to generate valid and correct patches for real-world vulnerabilities. In this paper, we leverage the power and merits of pre-trained language language models (LLMs) to enable automated vulnerability patching using no test input/exploit evidence and without model training/fine-tuning. To elicit LLMs to effectively reason about vulnerable code behaviors, which is essential for quality patch generation, we introduce vulnerability semantics reasoning and adaptive prompting on LLMs and instantiate the methodology as APPATCH, an automated LLM-based patching system. Our evaluation of APPATCH on 97 zero-day vulnerabilities and 20 existing vulnerabilities demonstrates its superior performance to both existing prompting methods and state-of-the-art non-LLM-based techniques (by up to 28.33% in F1 and 182.26% in recall over the best baseline). Through APPATCH, we demonstrate what helps for LLM-based patching and how, as well as discussing what still lacks and why. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13597 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | APPATCH: Automated Adaptive Prompting Large Language Models for Real-World Software Vulnerability Patching Nong, Yu Yang, Haoran Cheng, Long Hu, Hongxin Cai, Haipeng Cryptography and Security Software Engineering Timely and effective vulnerability patching is essential for cybersecurity defense, for which various approaches have been proposed yet still struggle to generate valid and correct patches for real-world vulnerabilities. In this paper, we leverage the power and merits of pre-trained language language models (LLMs) to enable automated vulnerability patching using no test input/exploit evidence and without model training/fine-tuning. To elicit LLMs to effectively reason about vulnerable code behaviors, which is essential for quality patch generation, we introduce vulnerability semantics reasoning and adaptive prompting on LLMs and instantiate the methodology as APPATCH, an automated LLM-based patching system. Our evaluation of APPATCH on 97 zero-day vulnerabilities and 20 existing vulnerabilities demonstrates its superior performance to both existing prompting methods and state-of-the-art non-LLM-based techniques (by up to 28.33% in F1 and 182.26% in recall over the best baseline). Through APPATCH, we demonstrate what helps for LLM-based patching and how, as well as discussing what still lacks and why. |
| title | APPATCH: Automated Adaptive Prompting Large Language Models for Real-World Software Vulnerability Patching |
| topic | Cryptography and Security Software Engineering |
| url | https://arxiv.org/abs/2408.13597 |