An Exploratory Study on Fine-Tuning Large Language Models for Secure Code Generation

• URL: http://arxiv.org/abs/2408.09078v1
• Date: Sat, 17 Aug 2024 02:51:27 GMT
• Title: An Exploratory Study on Fine-Tuning Large Language Models for Secure Code Generation
• Authors: Junjie Li, Fazle Rabbi, Cheng Cheng, Aseem Sangalay, Yuan Tian, Jinqiu Yang,
• Abstract summary: We present an exploratory study on whether fine-tuning pre-trained LLMs on datasets of vulnerability-fixing commits can promote secure code generation. We crawled a fine-tuning dataset for secure code generation by collecting code fixes of confirmed vulnerabilities from open-source repositories. Our exploration reveals that fine-tuning LLMs can improve secure code generation by 6.4% in C language and 5.4% in C++ language.
• Score: 17.69409515806874
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI-powered coding assistants such as GitHub Copilot and OpenAI ChatGPT have achieved notable success in automating code generation. However, these tools rely on pre-trained Large Language Models (LLMs) that are typically trained on human-written code sourced from open-source project hosting sites like GitHub, which often contains inherent security vulnerabilities. These vulnerabilities may then be mirrored in the code generated by these LLMs, a critical risk revealed and highlighted by recent empirical studies. In this work, we present an exploratory study on whether fine-tuning pre-trained LLMs on datasets of vulnerability-fixing commits can promote secure code generation. We explored two parameter-efficient fine-tuning techniques (LoRa and IA3) on two pre-trained LLMs for code generation. We crawled a fine-tuning dataset (14,622 C and C++ files) for secure code generation by collecting code fixes of confirmed vulnerabilities from open-source repositories. Our evaluation dataset comprises 52 vulnerability scenarios designed to cover the top most dangerous C and C++ Common Weakness Enumerations (CWEs). Each scenario is a prompt that may induce LLMs to generate vulnerable code. Our exploration reveals that fine-tuning LLMs can improve secure code generation by 6.4% in C language and 5.4% in C++ language. We further experimented with fine-tuning LLMs using different versions of the collected secure code dataset (block, function, and line). We found that fine-tuning with function-level and block-level datasets achieves the best secure code generation performance, compared to the alternatives (file-level and line-level).

Related papers

• ProSec: Fortifying Code LLMs with Proactive Security Alignment [14.907702430331803]
  Security of code-specific large language models (LLMs) remains under-explored. We propose ProSec, a novel security alignment approach designed to align code LLMs with secure coding practices. Experiments show that models trained with ProSec is 29.2% to 35.5% more secure compared to previous work.
  arXiv  Detail & Related papers  (2024-11-19T22:00:01Z)
• OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models [70.72097493954067]
  Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems. While open-access code LLMs are increasingly approaching the performance levels of proprietary models, high-quality code LLMs remain limited. We introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an "open cookbook" for the research community.
  arXiv  Detail & Related papers  (2024-11-07T17:47:25Z)
• HexaCoder: Secure Code Generation via Oracle-Guided Synthetic Training Data [60.75578581719921]
  Large language models (LLMs) have shown great potential for automatic code generation. Recent studies highlight that many LLM-generated code contains serious security vulnerabilities. We introduce HexaCoder, a novel approach to enhance the ability of LLMs to generate secure codes.
  arXiv  Detail & Related papers  (2024-09-10T12:01:43Z)
• Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval [20.959848710829878]
  Large language models (LLMs) have brought significant advancements to code generation and code repair. However, their training using unsanitized data from open-source repositories, like GitHub, raises the risk of inadvertently propagating security vulnerabilities. We aim to present a comprehensive study aimed at precisely evaluating and enhancing the security aspects of code LLMs.
  arXiv  Detail & Related papers  (2024-07-02T16:13:21Z)
• Can We Trust Large Language Models Generated Code? A Framework for In-Context Learning, Security Patterns, and Code Evaluations Across Diverse LLMs [2.7138982369416866]
  Large Language Models (LLMs) have revolutionized automated code generation in software engineering. However, concerns have arisen regarding the security and quality of the generated code. Our research aims to tackle these issues by introducing a framework for secure behavioral learning of LLMs.
  arXiv  Detail & Related papers  (2024-06-18T11:29:34Z)
• VersiCode: Towards Version-controllable Code Generation [58.82709231906735]
  Large Language Models (LLMs) have made tremendous strides in code generation, but existing research fails to account for the dynamic nature of software development. We propose two novel tasks aimed at bridging this gap: version-specific code completion (VSCC) and version-aware code migration (VACM) We conduct an extensive evaluation on VersiCode, which reveals that version-controllable code generation is indeed a significant challenge.
  arXiv  Detail & Related papers  (2024-06-11T16:15:06Z)
• CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion [117.178835165855]
  This paper introduces CodeAttack, a framework that transforms natural language inputs into code inputs. Our studies reveal a new and universal safety vulnerability of these models against code input. We find that a larger distribution gap between CodeAttack and natural language leads to weaker safety generalization.
  arXiv  Detail & Related papers  (2024-03-12T17:55:38Z)
• SALLM: Security Assessment of Generated Code [0.5137309756089941]
  This paper describes SALLM, a framework to benchmark Large Language Models' abilities to generate secure code systematically. The framework has three major components: a novel dataset of security-centric Python prompts, assessment techniques to evaluate the generated code, and novel metrics to evaluate the models' performance from the perspective of secure code generation.
  arXiv  Detail & Related papers  (2023-11-01T22:46:31Z)
• Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs [59.596335292426105]
  This paper collects the first open-source dataset to evaluate safeguards in large language models. We train several BERT-like classifiers to achieve results comparable with GPT-4 on automatic safety evaluation.
  arXiv  Detail & Related papers  (2023-08-25T14:02:12Z)
• CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models [58.27254444280376]
  Large language models (LLMs) for automatic code generation have achieved breakthroughs in several programming tasks. Training data for these models is usually collected from the Internet (e.g., from open-source repositories) and is likely to contain faults and security vulnerabilities. This unsanitized training data can cause the language models to learn these vulnerabilities and propagate them during the code generation procedure.
  arXiv  Detail & Related papers  (2023-02-08T11:54:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.