Toss a Fault to Your Witcher: Applying Grey-box Coverage-Guided Mutational Fuzzing to Detect SQL and Command Injection Vulnerabilities
Erik Trickel, Fabio Pagani, Chang Zhu, Lukas Dresel, Giovanni Vigna, Christopher Kruegel, Ruoyu Wang, Tiffany Bao, Yan Shoshitaishvili, Adam Doupé
Abstract
Black-box web application vulnerability scanners attempt to automatically identify vulnerabilities in web applications without access to the source code. However, they do so by using a manually curated list of vulnerability-inducing inputs, which significantly reduces the ability of a black-box scanner to explore the web application’s input space and which can cause false negatives. In addition, black-box scanners must attempt to infer that a vulnerability was triggered, which causes false positives.To overcome these limitations, we propose Witcher, a novel web vulnerability discovery framework that is inspired by grey-box coverage-guided fuzzing. Witcher implements the concept of fault escalation to detect both SQL and command injection vulnerabilities. Additionally, Witcher captures coverage information and creates output-derived input guidance to focus the input generation and, therefore, to increase the state-space exploration of the web application. On a dataset of 18 web applications written in PHP, Python, Node.js, Java, Ruby, and C, 13 of which had known vulnerabilities, Witcher was able to find 23 of the 36 known vulnerabilities (64%), and additionally found 67 previously unknown vulnerabilities, 4 of which received CVE numbers. In our experiments, Witcher outperformed state of the art scanners both in terms of number of vulnerabilities found, but also in terms of coverage of web applications.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 8f2fc984-620a-4fa1-a71a-7e77ca5cc0bbCited by top-tier papers31
- Atropos: Effective Fuzzing of Web Applications for Server-Side VulnerabilitiesEmre Güler, Sergej Schumilo, Moritz Schloegel, Nils Bars et al.USENIX Security 2024 · 45 citations
- Undefined-oriented Programming: Detecting and Chaining Prototype Pollution Gadgets in Node.js Template Engines for Malicious ConsequencesZhengyu Liu, Kecheng An, Yinzhi CaoS&P 2024 · 17 citations
- Where URLs Become Weapons: Automated Discovery of SSRF Vulnerabilities in Web ApplicationsEnze Wang, Jianjun Chen, Wei Xie, Chuhan Wang et al.S&P 2024 · 15 citations
- RecurScan: Detecting Recurring Vulnerabilities in PHP Web ApplicationsYoukun Shi, Yuan Zhang, Tianhao Bai, Lei Zhang et al.WWW 2024 · 12 citations
- The Matter of Captchas: An Analysis of a Brittle Security Feature on the Modern WebBehzad Ousat, Esteban Schafir, Duc C. Hoang, Mohammad Ali Tofighi et al.WWW 2024 · 11 citations
Builds on8
- Driller: Augmenting Fuzzing Through Selective Symbolic ExecutionNick Stephens, John Grosen, Christopher Salls, Andrew Dutcher et al.NDSS 2016 · 1,021 citations
- Evaluating Fuzz TestingGeorge Klees, Andrew Ruef, Benji Cooper, Shiyi Wei et al.CCS 2018 · 753 citations
- Towards Automated Dynamic Analysis for Linux-based Embedded FirmwareDaming D. Chen, Maverick Woo, David Brumley, Manuel EgeleNDSS 2016 · 428 citations
- T-Fuzz: Fuzzing by Program TransformationHui Peng, Yan Shoshitaishvili, Mathias PayerS&P 2018 · 326 citations
- NAUTILUS: Fishing for Deep Bugs with GrammarsCornelius Aschermann, Tommaso Frassetto, Thorsten Holz, Patrick Jauernig et al.NDSS 2019 · 291 citations
Related papers
- Black Widow: Blackbox Data-driven Web ScanningBenjamin Eriksson, Giancarlo Pellegrino, Andrei SabelfeldS&P 2021 · 65 citations
- Zelda: Feedback-driven Closed-box Fuzzing for Identifying Web Application VulnerabilitiesSoyoung Lee, Sunnyeo Park, Yonghwi Kwon, Sooel SonWWW 2026
- ReScan: A Middleware Framework for Realistic and Robust Black-box Web Application ScanningKostas Drakonakis, Sotiris Ioannidis, Jason PolakisNDSS 2023
- Predator: Directed Web Application Fuzzing for Efficient Vulnerability ValidationChenlin Wang, Wei Meng, Changhua Luo, Penghui LiS&P 2025
- YuraScanner: Leveraging LLMs for Task-driven Web App ScanningAleksei Stafeev, Tim Recktenwald, Gianluca De Stefano, Soheil Khodayari et al.NDSS 2025
