Buchumschlag

Enhancing Security in Third-Party Library Reuse -- Comprehensive Detection of 1-day Vulnerability through Code Patch Analysis

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Xu, Shangzhi, Dong, Jialiang, Cai, Weiting, Li, Juanru, Shaghaghi, Arash, Sun, Nan, Ma, Siqi
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Software Engineering
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Does the Vulnerability Threaten Our Projects? Automated Vulnerable API Detection for Third-Party Libraries
von: Zhang, Fangyuan, et al.
Veröffentlicht: (2024)

Vulnerability-Triggering Test Case Generation from Third-Party Libraries
von: Gao, Yi, et al.
Veröffentlicht: (2024)

A^3-CodGen: A Repository-Level Code Generation Framework for Code Reuse with Local-Aware, Global-Aware, and Third-Party-Library-Aware
von: Liao, Dianshu, et al.
Veröffentlicht: (2023)

A Survey of Third-Party Library Security Research in Application Software
von: Zeng, Jia, et al.
Veröffentlicht: (2024)

Drop the Golden Apples: Identifying Third-Party Reuse by DB-Less Software Composition Analysis
von: Zhang, Lyuye, et al.
Veröffentlicht: (2025)

BinCoFer: Three-Stage Purification for Effective C/C++ Binary Third-Party Library Detection
von: Zou, Yayi, et al.
Veröffentlicht: (2025)

M2CVD: Enhancing Vulnerability Semantic through Multi-Model Collaboration for Code Vulnerability Detection
von: Wang, Ziliang, et al.
Veröffentlicht: (2024)

A Slicing-Based Approach for Detecting and Patching Vulnerable Code Clones
von: Alomari, Hakam, et al.
Veröffentlicht: (2025)

Similar but Patched Code Considered Harmful -- The Impact of Similar but Patched Code on Recurring Vulnerability Detection and How to Remove Them
von: Tan, Zixuan, et al.
Veröffentlicht: (2024)

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs
von: Di Sipio, Claudio, et al.
Veröffentlicht: (2025)

JC-Finder: Detecting Java Clone-based Third-Party Library by Class-level Tree Analysis
von: Zhao, Lida, et al.
Veröffentlicht: (2025)

Version-level Third-Party Library Detection in Android Applications via Class Structural Similarity
von: Zhou, Bolin, et al.
Veröffentlicht: (2025)

AutoEG: Exploiting Known Third-Party Vulnerabilities in Black-Box Web Applications
von: Yang, Ruozhao, et al.
Veröffentlicht: (2026)

Assessing Privacy Compliance of Android Third-Party SDKs
von: Meng, Mark Huasong, et al.
Veröffentlicht: (2024)

Tracking GPTs Third Party Service: Automation, Analysis, and Insights
von: Yan, Chuan, et al.
Veröffentlicht: (2025)

An Interview Study on Third-Party Cyber Threat Hunting Processes in the U.S. Department of Homeland Security
von: Maxam III, William P., et al.
Veröffentlicht: (2024)

Match & Mend: Minimally Invasive Local Reassembly for Patching N-day Vulnerabilities in ARM Binaries
von: Jänich, Sebastian, et al.
Veröffentlicht: (2025)

Improving Data Curation of Software Vulnerability Patches through Uncertainty Quantification
von: Chen, Hui, et al.
Veröffentlicht: (2024)

Line-level Semantic Structure Learning for Code Vulnerability Detection
von: Wang, Ziliang, et al.
Veröffentlicht: (2024)

PatchFinder: A Two-Phase Approach to Security Patch Tracing for Disclosed Vulnerabilities in Open-Source Software
von: Li, Kaixuan, et al.
Veröffentlicht: (2024)

Patch Validation in Automated Vulnerability Repair
von: Yu, Zheng, et al.
Veröffentlicht: (2026)

SmartPatchLinker: An Open-Source Tool to Linked Changes Detection for Code Review
von: Khemissi, Islem, et al.
Veröffentlicht: (2026)

Improving Automated Secure Code Reviews: A Synthetic Dataset for Code Vulnerability Flaws
von: Centellas-Claros, Leonardo, et al.
Veröffentlicht: (2025)

Empirical Study of Code Large Language Models for Binary Security Patch Detection
von: Li, Qingyuan, et al.
Veröffentlicht: (2025)

From Detection to Prevention: Explaining Security-Critical Code to Avoid Vulnerabilities
von: Krishnamurthy, Ranjith, et al.
Veröffentlicht: (2026)

Bake Two Cakes with One Oven: RL for Defusing Popularity Bias and Cold-start in Third-Party Library Recommendations
von: Vuong, Minh Hoang, et al.
Veröffentlicht: (2025)

VFDelta: A Framework for Detecting Silent Vulnerability Fixes by Enhancing Code Change Learning
von: Yang, Xu, et al.
Veröffentlicht: (2024)

Repository-Level Graph Representation Learning for Enhanced Security Patch Detection
von: Wen, Xin-Cheng, et al.
Veröffentlicht: (2024)

Enhancing Large Language Models with Faster Code Preprocessing for Vulnerability Detection
von: Gonçalves, José, et al.
Veröffentlicht: (2025)

Optimizing Code Embeddings and ML Classifiers for Python Source Code Vulnerability Detection
von: Farasat, Talaya, et al.
Veröffentlicht: (2025)

Wired for Reuse: Automating Context-Aware Code Adaptation in IDEs via LLM-Based Agent
von: Wang, Taiming, et al.
Veröffentlicht: (2025)

Stdlib or Third-Party? Empirical Performance and Correctness of LLM-Assisted Zero-Dependency Python Libraries
von: Ding, Peng, et al.
Veröffentlicht: (2026)

Coding-PTMs: How to Find Optimal Code Pre-trained Models for Code Embedding in Vulnerability Detection?
von: Zhao, Yu, et al.
Veröffentlicht: (2024)

AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection
von: Charoenwet, Wachiraphan, et al.
Veröffentlicht: (2026)

Enhancing Software Vulnerability Detection Using Code Property Graphs and Convolutional Neural Networks
von: Saimbhi, Amanpreet Singh
Veröffentlicht: (2025)

Evaluating Pre-Trained Models for Multi-Language Vulnerability Patching
von: Khan, Zanis Ali, et al.
Veröffentlicht: (2025)

Investigating Code Reuse in Software Redesign: A Case Study
von: Zhang, Xiaowen, et al.
Veröffentlicht: (2026)

Revisiting Vulnerability Patch Identification on Data in the Wild
von: Irsan, Ivana Clairine, et al.
Veröffentlicht: (2026)

TransLibEval: Demystify Large Language Models' Capability in Third-party Library-targeted Code Translation
von: Xue, Pengyu, et al.
Veröffentlicht: (2025)

Ensembling Large Language Models for Code Vulnerability Detection: An Empirical Evaluation
von: Sun, Zhihong, et al.
Veröffentlicht: (2025)