VulZoo: A Comprehensive Vulnerability Intelligence Dataset

• URL: http://arxiv.org/abs/2406.16347v2
• Date: Tue, 24 Sep 2024 00:54:30 GMT
• Title: VulZoo: A Comprehensive Vulnerability Intelligence Dataset
• Authors: Bonan Ruan, Jiahao Liu, Weibo Zhao, Zhenkai Liang,
• Abstract summary: VulZoo is a comprehensive vulnerability intelligence dataset that covers 17 popular vulnerability information sources. We make VulZoo publicly available and maintain it with incremental updates to facilitate future research.
• Score: 12.229092589037808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Software vulnerabilities pose critical security and risk concerns for many software systems. Many techniques have been proposed to effectively assess and prioritize these vulnerabilities before they cause serious consequences. To evaluate their performance, these solutions often craft their own experimental datasets from limited information sources, such as MITRE CVE and NVD, lacking a global overview of broad vulnerability intelligence. The repetitive data preparation process further complicates the verification and comparison of new solutions. To resolve this issue, in this paper, we propose VulZoo, a comprehensive vulnerability intelligence dataset that covers 17 popular vulnerability information sources. We also construct connections among these sources, enabling more straightforward configuration and adaptation for different vulnerability assessment tasks (e.g., vulnerability type prediction). Additionally, VulZoo provides utility scripts for automatic data synchronization and cleaning, relationship mining, and statistics generation. We make VulZoo publicly available and maintain it with incremental updates to facilitate future research. We believe that VulZoo serves as a valuable input to vulnerability assessment and prioritization studies. The dataset with utility scripts is available at https://github.com/NUS-Curiosity/VulZoo.

Related papers

• Improving Data Curation of Software Vulnerability Patches through Uncertainty Quantification [6.916509590637601]
  We propose an approach employing Uncertainty Quantification (UQ) to curate datasets of publicly-available software vulnerability patches. Model Ensemble and heteroscedastic models are the best choices for vulnerability patch datasets.
  arXiv  Detail & Related papers  (2024-11-18T15:37:28Z)
• RealVul: Can We Detect Vulnerabilities in Web Applications with LLM? [4.467475584754677]
  We present RealVul, the first LLM-based framework designed for PHP vulnerability detection. We can isolate potential vulnerability triggers while streamlining the code and eliminating unnecessary semantic information. We also address the issue of insufficient PHP vulnerability samples by improving data synthesis methods.
  arXiv  Detail & Related papers  (2024-10-10T03:16:34Z)
• KGV: Integrating Large Language Models with Knowledge Graphs for Cyber Threat Intelligence Credibility Assessment [38.312774244521]
  We propose a knowledge graph-based verifier for Cyber Threat Intelligence (CTI) quality assessment framework. Our approach introduces Large Language Models (LLMs) to automatically extract OSCTI key claims to be verified. To fill the gap in the research field, we created and made public the first dataset for threat intelligence assessment from heterogeneous sources.
  arXiv  Detail & Related papers  (2024-08-15T11:32:46Z)
• ARVO: Atlas of Reproducible Vulnerabilities for Open Source Software [20.927909014593318]
  We introduce ARVO: an Atlas of Reproducible Vulnerabilities in Open-source software. We reproduce more than 5,000 memory vulnerabilities across over 250 projects. Our dataset can be automatically updated as OSS-Fuzz finds new vulnerabilities.
  arXiv  Detail & Related papers  (2024-08-04T22:13:14Z)
• "Glue pizza and eat rocks" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models [74.05368440735468]
  Retrieval-Augmented Generative (RAG) models enhance Large Language Models (LLMs) In this paper, we demonstrate a security threat where adversaries can exploit the openness of these knowledge bases.
  arXiv  Detail & Related papers  (2024-06-26T05:36:23Z)
• On Security Weaknesses and Vulnerabilities in Deep Learning Systems [32.14068820256729]
  We specifically look into deep learning (DL) framework and perform the first systematic study of vulnerabilities in DL systems. We propose a two-stream data analysis framework to explore vulnerability patterns from various databases. We conducted a large-scale empirical study of 3,049 DL vulnerabilities to better understand the patterns of vulnerability and the challenges in fixing them.
  arXiv  Detail & Related papers  (2024-06-12T23:04:13Z)
• Vulnerability Detection with Code Language Models: How Far Are We? [40.455600722638906]
  PrimeVul is a new dataset for training and evaluating code LMs for vulnerability detection. It incorporates a novel set of data labeling techniques that achieve comparable label accuracy to human-verified benchmarks. It also implements a rigorous data de-duplication and chronological data splitting strategy to mitigate data leakage issues.
  arXiv  Detail & Related papers  (2024-03-27T14:34:29Z)
• Profile of Vulnerability Remediations in Dependencies Using Graph Analysis [40.35284812745255]
  This research introduces graph analysis methods and a modified Graph Attention Convolutional Neural Network (GAT) model. We analyze control flow graphs to profile breaking changes in applications occurring from dependency upgrades intended to remediate vulnerabilities. Results demonstrate the effectiveness of the enhanced GAT model in offering nuanced insights into the relational dynamics of code vulnerabilities.
  arXiv  Detail & Related papers  (2024-03-08T02:01:47Z)
• REEF: A Framework for Collecting Real-World Vulnerabilities and Fixes [40.401211102969356]
  We propose an automated collecting framework REEF to collect REal-world vulnErabilities and Fixes from open-source repositories. We develop a multi-language crawler to collect vulnerabilities and their fixes, and design metrics to filter for high-quality vulnerability-fix pairs. Through extensive experiments, we demonstrate that our approach can collect high-quality vulnerability-fix pairs and generate strong explanations.
  arXiv  Detail & Related papers  (2023-09-15T02:50:08Z)
• DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection [29.52887618905746]
  This dataset contains 18,945 vulnerable functions spanning 150 CWEs and 330,492 non-vulnerable functions extracted from 7,514 commits. Our results show that deep learning is still not ready for vulnerability detection, due to high false positive rate, low F1 score, and difficulty of detecting hard CWEs. We demonstrate that large language models (LLMs) are a promising research direction for ML-based vulnerability detection, outperforming Graph Neural Networks (GNNs) with code-structure features.
  arXiv  Detail & Related papers  (2023-04-01T23:29:14Z)
• VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements [62.93814803258067]
  This paper presents VELVET, a novel ensemble learning approach to locate vulnerable statements in source code. Our model combines graph-based and sequence-based neural networks to successfully capture the local and global context of a program graph. VELVET achieves 99.6% and 43.6% top-1 accuracy over synthetic data and real-world data, respectively.
  arXiv  Detail & Related papers  (2021-12-20T22:45:27Z)

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.