An Accurate and Efficient Vulnerability Propagation Analysis Framework
- URL: http://arxiv.org/abs/2506.01342v1
- Date: Mon, 02 Jun 2025 05:55:45 GMT
- Title: An Accurate and Efficient Vulnerability Propagation Analysis Framework
- Authors: Bonan Ruan, Zhiwei Lin, Jiahao Liu, Chuqi Zhang, Kaihang Ji, Zhenkai Liang,
- Abstract summary: We propose a novel approach to quantify the scope and evolution of vulnerability impacts in software supply chains.<n>We implement a prototype of our approach in the Java Maven ecosystem and evaluate it on 100 real-world vulnerabilities.
- Score: 13.051314477680902
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Identifying the impact scope and scale is critical for software supply chain vulnerability assessment. However, existing studies face substantial limitations. First, prior studies either work at coarse package-level granularity, producing many false positives, or fail to accomplish whole-ecosystem vulnerability propagation analysis. Second, although vulnerability assessment indicators like CVSS characterize individual vulnerabilities, no metric exists to specifically quantify the dynamic impact of vulnerability propagation across software supply chains. To address these limitations and enable accurate and comprehensive vulnerability impact assessment, we propose a novel approach: (i) a hierarchical worklist-based algorithm for whole-ecosystem and call-graph-level vulnerability propagation analysis and (ii) the Vulnerability Propagation Scoring System (VPSS), a dynamic metric to quantify the scope and evolution of vulnerability impacts in software supply chains. We implement a prototype of our approach in the Java Maven ecosystem and evaluate it on 100 real-world vulnerabilities. Experimental results demonstrate that our approach enables effective ecosystem-wide vulnerability propagation analysis, and provides a practical, quantitative measure of vulnerability impact through VPSS.
Related papers
- White-Basilisk: A Hybrid Model for Code Vulnerability Detection [50.49233187721795]
We introduce White-Basilisk, a novel approach to vulnerability detection that demonstrates superior performance.<n>White-Basilisk achieves results in vulnerability detection tasks with a parameter count of only 200M.<n>This research establishes new benchmarks in code security and provides empirical evidence that compact, efficiently designed models can outperform larger counterparts in specialized tasks.
arXiv Detail & Related papers (2025-07-11T12:39:25Z) - Vulnerability Assessment Combining CVSS Temporal Metrics and Bayesian Networks [0.0]
This work presents an innovative approach by incorporating the temporal dimension into vulnerability assessment.<n>The proposed approach dynamically computes the Temporal Score and updates the CVSS Base Score by processing data on exploits and fixes from vulnerability databases.
arXiv Detail & Related papers (2025-06-23T14:53:17Z) - Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection [38.083049237330826]
This study explores the use of Large Language Models (LLMs) in software vulnerability assessment by simulating the identification of Python code with known Common Weaknessions (CWEs)<n>Our results indicate that while zero-shot prompting performs poorly, few-shot prompting significantly enhances classification performance.<n> challenges such as model reliability, interpretability, and adversarial robustness remain critical areas for future research.
arXiv Detail & Related papers (2025-06-11T18:43:51Z) - Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systems [44.61435605872856]
We introduce a practical robustness definition grounded in distributional robustness, explicitly tailored to industrial CPS.<n>Our framework simulates realistic disturbances, such as sensor drift, noise and irregular sampling, enabling thorough robustness analyses of forecasting models.
arXiv Detail & Related papers (2025-04-04T14:50:48Z) - Beyond the Surface: An NLP-based Methodology to Automatically Estimate CVE Relevance for CAPEC Attack Patterns [42.63501759921809]
We propose a methodology leveraging Natural Language Processing (NLP) to associate Common Vulnerabilities and Exposure (CAPEC) vulnerabilities with Common Attack Patternion and Classification (CAPEC) attack patterns.<n> Experimental evaluations demonstrate superior performance compared to state-of-the-art models.
arXiv Detail & Related papers (2025-01-13T08:39:52Z) - Bringing Order Amidst Chaos: On the Role of Artificial Intelligence in Secure Software Engineering [0.0]
The ever-evolving technological landscape offers both opportunities and threats, creating a dynamic space where chaos and order compete.<n>Secure software engineering (SSE) must continuously address vulnerabilities that endanger software systems.<n>This thesis seeks to bring order to the chaos in SSE by addressing domain-specific differences that impact AI accuracy.
arXiv Detail & Related papers (2025-01-09T11:38:58Z) - CRepair: CVAE-based Automatic Vulnerability Repair Technology [1.147605955490786]
Software vulnerabilities pose significant threats to the integrity, security, and reliability of modern software and its application data.
To address the challenges of vulnerability repair, researchers have proposed various solutions, with learning-based automatic vulnerability repair techniques gaining widespread attention.
This paper proposes CRepair, a CVAE-based automatic vulnerability repair technology aimed at fixing security vulnerabilities in system code.
arXiv Detail & Related papers (2024-11-08T12:55:04Z) - The Impact of SBOM Generators on Vulnerability Assessment in Python: A Comparison and a Novel Approach [56.4040698609393]
Software Bill of Materials (SBOM) has been promoted as a tool to increase transparency and verifiability in software composition.
Current SBOM generation tools often suffer from inaccuracies in identifying components and dependencies.
We propose PIP-sbom, a novel pip-inspired solution that addresses their shortcomings.
arXiv Detail & Related papers (2024-09-10T10:12:37Z) - SecScore: Enhancing the CVSS Threat Metric Group with Empirical Evidences [0.0]
One of the most widely used vulnerability scoring systems (CVSS) does not address the increasing likelihood of emerging an exploit code.
We present SecScore, an innovative vulnerability severity score that enhances CVSS Threat metric group.
arXiv Detail & Related papers (2024-05-14T12:25:55Z) - Dynamic Vulnerability Criticality Calculator for Industrial Control Systems [0.0]
This paper introduces an innovative approach by proposing a dynamic vulnerability criticality calculator.
Our methodology encompasses the analysis of environmental topology and the effectiveness of deployed security mechanisms.
Our approach integrates these factors into a comprehensive Fuzzy Cognitive Map model, incorporating attack paths to holistically assess the overall vulnerability score.
arXiv Detail & Related papers (2024-03-20T09:48:47Z) - 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) - 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) - Differential privacy and robust statistics in high dimensions [49.50869296871643]
High-dimensional Propose-Test-Release (HPTR) builds upon three crucial components: the exponential mechanism, robust statistics, and the Propose-Test-Release mechanism.
We show that HPTR nearly achieves the optimal sample complexity under several scenarios studied in the literature.
arXiv Detail & Related papers (2021-11-12T06:36:40Z) - Autosploit: A Fully Automated Framework for Evaluating the
Exploitability of Security Vulnerabilities [47.748732208602355]
Autosploit is an automated framework for evaluating the exploitability of vulnerabilities.
It automatically tests the exploits on different configurations of the environment.
It is able to identify the system properties that affect the ability to exploit a vulnerability in both noiseless and noisy environments.
arXiv Detail & Related papers (2020-06-30T18:49:18Z)
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.