Content area

Abstract

Web applications play a crucial role in modern businesses, offering various services and often exposing sensitive data that can be enticing to attackers. As a result, there is a growing interest in finding innovative approaches for discovering vulnerabilities in web applications. In the evolving landscape of web security, the realm of fuzz testing has garnered substantial attention for its effectiveness in identifying vulnerabilities. However, existing literature has often underemphasized the nuances of web-centric fuzzing methodologies. This article presents a comprehensive exploration of fuzzing techniques specifically tailored to web applications, addressing the gap in the current research. Our work presents a holistic perspective on web-centric fuzzing, introduces a modular architecture that improves fuzzing effectiveness, demonstrates the reusability of certain fuzzing steps, and offers an open-source software package for the broader security community. By addressing these key contributions, we aim to facilitate advancements in web application security, empower researchers to explore new fuzzing techniques, and ultimately enhance the overall cybersecurity landscape.

Details

Title
Sniping at web applications to discover input-handling vulnerabilities
Author
Brandi, Ciro 1 ; Perrone, Gaetano 1 ; Romano, Simon Pietro 1   VIAFID ORCID Logo 

 University of Napoli Federico II, Department of Electrical Engineering and Information Technology, Naples, Italy (GRID:grid.4691.a) (ISNI:0000 0001 0790 385X) 
Volume
20
Issue
4
Pages
641-667
Publication year
2024
Publication date
Nov 2024
Publisher
Springer Nature B.V.
Place of publication
Heidelberg
Country of publication
Netherlands
Publication subject
ISSN
22638733
e-ISSN
17729904
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2024-04-12
Milestone dates
2024-03-10 (Registration); 2023-04-19 (Received); 2024-02-21 (Accepted)
Publication history
 
 
   First posting date
12 Apr 2024
ProQuest document ID
3254740403
Document URL
https://www.proquest.com/scholarly-journals/sniping-at-web-applications-discover-input/docview/3254740403/se-2?accountid=208611
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-09-27
Database
ProQuest One Academic