VulnLLMEval: A Framework for Evaluating Large Language Models in Software Vulnerability Detection and Patching
- URL: http://arxiv.org/abs/2409.10756v1
- Date: Mon, 16 Sep 2024 22:00:20 GMT
- Title: VulnLLMEval: A Framework for Evaluating Large Language Models in Software Vulnerability Detection and Patching
- Authors: Arastoo Zibaeirad, Marco Vieira,
- Abstract summary: Large Language Models (LLMs) have shown promise in tasks like code translation.
This paper introduces VulnLLMEval, a framework designed to assess the performance of LLMs in identifying and patching vulnerabilities in C code.
Our study includes 307 real-world vulnerabilities extracted from the Linux kernel.
- Score: 0.9208007322096533
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large Language Models (LLMs) have shown promise in tasks like code translation, prompting interest in their potential for automating software vulnerability detection (SVD) and patching (SVP). To further research in this area, establishing a benchmark is essential for evaluating the strengths and limitations of LLMs in these tasks. Despite their capabilities, questions remain regarding whether LLMs can accurately analyze complex vulnerabilities and generate appropriate patches. This paper introduces VulnLLMEval, a framework designed to assess the performance of LLMs in identifying and patching vulnerabilities in C code. Our study includes 307 real-world vulnerabilities extracted from the Linux kernel, creating a well-curated dataset that includes both vulnerable and patched code. This dataset, based on real-world code, provides a diverse and representative testbed for evaluating LLM performance in SVD and SVP tasks, offering a robust foundation for rigorous assessment. Our results reveal that LLMs often struggle with distinguishing between vulnerable and patched code. Furthermore, in SVP tasks, these models tend to oversimplify the code, producing solutions that may not be directly usable without further refinement.
Related papers
- LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights [12.424610893030353]
Large Language Models (LLMs) are emerging as transformative tools for software vulnerability detection.
This paper provides a detailed survey of LLMs in vulnerability detection.
We address challenges such as cross-language vulnerability detection, multimodal data integration, and repository-level analysis.
arXiv Detail & Related papers (2025-02-10T21:33:38Z) - Large Language Models and Code Security: A Systematic Literature Review [0.0]
Large Language Models (LLMs) have emerged as powerful tools for automating various programming tasks.
LLMs could introduce vulnerabilities unbeknown to the programmer.
When analyzing code, they could miss clear vulnerabilities or signal nonexistent ones.
arXiv Detail & Related papers (2024-12-19T16:20:22Z) - Exploring Automatic Cryptographic API Misuse Detection in the Era of LLMs [60.32717556756674]
This paper introduces a systematic evaluation framework to assess Large Language Models in detecting cryptographic misuses.
Our in-depth analysis of 11,940 LLM-generated reports highlights that the inherent instabilities in LLMs can lead to over half of the reports being false positives.
The optimized approach achieves a remarkable detection rate of nearly 90%, surpassing traditional methods and uncovering previously unknown misuses in established benchmarks.
arXiv Detail & Related papers (2024-07-23T15:31:26Z) - AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models [95.09157454599605]
Large Language Models (LLMs) are becoming increasingly powerful, but they still exhibit significant but subtle weaknesses.
Traditional benchmarking approaches cannot thoroughly pinpoint specific model deficiencies.
We introduce a unified framework, AutoDetect, to automatically expose weaknesses in LLMs across various tasks.
arXiv Detail & Related papers (2024-06-24T15:16:45Z) - VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models [12.465060623389151]
This study introduces a new benchmark, VulDetectBench, to assess the vulnerability detection capabilities of Large Language Models (LLMs)
The benchmark comprehensively evaluates LLM's ability to identify, classify, and locate vulnerabilities through five tasks of increasing difficulty.
Our benchmark effectively evaluates the capabilities of various LLMs at different levels in the specific task of vulnerability detection, providing a foundation for future research and improvements in this critical area of code security.
arXiv Detail & Related papers (2024-06-11T13:42:57Z) - M2CVD: Enhancing Vulnerability Semantic through Multi-Model Collaboration for Code Vulnerability Detection [52.4455893010468]
Large Language Models (LLMs) have strong capabilities in code comprehension, but fine-tuning costs and semantic alignment issues limit their project-specific optimization.
Code models such CodeBERT are easy to fine-tune, but it is often difficult to learn vulnerability semantics from complex code languages.
This paper introduces the Multi-Model Collaborative Vulnerability Detection approach (M2CVD) to improve the detection accuracy of code models.
arXiv Detail & Related papers (2024-06-10T00:05:49Z) - PPTC-R benchmark: Towards Evaluating the Robustness of Large Language
Models for PowerPoint Task Completion [96.47420221442397]
We construct adversarial user instructions by attacking user instructions at sentence, semantic, and multi-language levels.
We test 3 closed-source and 4 open-source LLMs using a benchmark that incorporates robustness settings.
We find that GPT-4 exhibits the highest performance and strong robustness in our benchmark.
arXiv Detail & Related papers (2024-03-06T15:33:32Z) - How Far Have We Gone in Vulnerability Detection Using Large Language
Models [15.09461331135668]
We introduce a comprehensive vulnerability benchmark VulBench.
This benchmark aggregates high-quality data from a wide range of CTF challenges and real-world applications.
We find that several LLMs outperform traditional deep learning approaches in vulnerability detection.
arXiv Detail & Related papers (2023-11-21T08:20:39Z) - Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities [12.82645410161464]
We evaluate the effectiveness of 16 pre-trained Large Language Models on 5,000 code samples from five diverse security datasets.
Overall, LLMs show modest effectiveness in detecting vulnerabilities, obtaining an average accuracy of 62.8% and F1 score of 0.71 across datasets.
We find that advanced prompting strategies that involve step-by-step analysis significantly improve performance of LLMs on real-world datasets in terms of F1 score (by upto 0.18 on average)
arXiv Detail & Related papers (2023-11-16T13:17:20Z) - 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.