Code Vulnerability Detection: A Comparative Analysis of Emerging Large Language Models

• URL: http://arxiv.org/abs/2409.10490v1
• Date: Mon, 16 Sep 2024 17:23:00 GMT
• Title: Code Vulnerability Detection: A Comparative Analysis of Emerging Large Language Models
• Authors: Shaznin Sultana, Sadia Afreen, Nasir U. Eisty,
• Abstract summary: This paper investigates the effectiveness of Large Language Models (LLMs) in identifying vulnerabilities withins. We assess the performance of emerging LLMs, specifically Llama, CodeLlama, Gemma, and CodeGemma, alongside established state-of-the-art models BERT, RoBERTa, and GPT-3. We observe that CodeGemma achieves the highest F1-score of 58 and a Recall of 87, amongst the recent additions of large language models to detect software security vulnerabilities.
• Score: 0.46085106405479537
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing trend of vulnerability issues in software development as a result of a large dependence on open-source projects has received considerable attention recently. This paper investigates the effectiveness of Large Language Models (LLMs) in identifying vulnerabilities within codebases, with a focus on the latest advancements in LLM technology. Through a comparative analysis, we assess the performance of emerging LLMs, specifically Llama, CodeLlama, Gemma, and CodeGemma, alongside established state-of-the-art models such as BERT, RoBERTa, and GPT-3. Our study aims to shed light on the capabilities of LLMs in vulnerability detection, contributing to the enhancement of software security practices across diverse open-source repositories. We observe that CodeGemma achieves the highest F1-score of 58\ and a Recall of 87\, amongst the recent additions of large language models to detect software security vulnerabilities.

Related papers

• Attention Is All You Need for LLM-based Code Vulnerability Localization [30.532439965854774]
  Traditional methods for vulnerability localization, such as manual code audits or rule-based tools, are often time-consuming and limited in scope. This paper introduces LOVA, a novel framework leveraging the self-attention mechanisms inherent in large language models. We demonstrate that LOVA significantly outperforms existing LLM-based approaches, achieving up to a 5.3x improvement in F1-scores.
  arXiv  Detail & Related papers  (2024-10-20T05:02:18Z)
• Studying and Benchmarking Large Language Models For Log Level Suggestion [49.176736212364496]
  Large Language Models (LLMs) have become a focal point of research across various domains. This paper investigates the impact of characteristics and learning paradigms on the performance of 12 open-source LLMs in log level suggestion.
  arXiv  Detail & Related papers  (2024-10-11T03:52:17Z)
• Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection [9.652886240532741]
  This paper thoroughly analyses large language models' capabilities in detecting vulnerabilities within source code. We evaluate the performance of six open-source models that are specifically trained for vulnerability detection against six general-purpose LLMs.
  arXiv  Detail & Related papers  (2024-08-29T10:00:57Z)
• 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)
• What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [80.18342600996601]
  Large language models (LLMs) produce code that is shorter yet more complicated as compared to canonical solutions. We develop a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types. We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback.
  arXiv  Detail & Related papers  (2024-07-08T17:27:17Z)
• Harnessing Large Language Models for Software Vulnerability Detection: A Comprehensive Benchmarking Study [1.03590082373586]
  We propose using large language models (LLMs) to assist in finding vulnerabilities in source code. The aim is to test multiple state-of-the-art LLMs and identify the best prompting strategies. We find that LLMs can pinpoint many more issues than traditional static analysis tools, outperforming traditional tools in terms of recall and F1 scores.
  arXiv  Detail & Related papers  (2024-05-24T14:59:19Z)
• RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content [62.685566387625975]
  Current mitigation strategies, while effective, are not resilient under adversarial attacks. This paper introduces Resilient Guardrails for Large Language Models (RigorLLM), a novel framework designed to efficiently moderate harmful and unsafe inputs.
  arXiv  Detail & Related papers  (2024-03-19T07:25:02Z)
• 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)
• 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)
• Enhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation [24.668682498171776]
  Large language models (LLMs) have brought significant advancements to code generation, benefiting both novice and experienced developers. However, their training using unsanitized data from open-source repositories, like GitHub, introduces the risk of inadvertently propagating security vulnerabilities. This paper presents a comprehensive study focused on evaluating and enhancing code LLMs from a software security perspective.
  arXiv  Detail & Related papers  (2023-10-25T00:32:56Z)
• 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.