Thematische Bibliographien / Model fuzzing

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Auswahl der wissenschaftlichen Literatur zum Thema „Model fuzzing“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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Zeitschriftenartikel zum Thema "Model fuzzing"

Pan, Yan, Wei Lin, Liang Jiao, and Yuefei Zhu. "Model-Based Grey-Box Fuzzing of Network Protocols." Security and Communication Networks 2022 (May 5, 2022): 1–13. http://dx.doi.org/10.1155/2022/6880677.

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The widely used network protocols play a crucial role in various systems. However, the protocol vulnerabilities caused by the design of the network protocol or its implementation by programmers lead to multiple security incidents and substantial losses. Hence, it is important to study the protocol fuzzing in order to ensure its correctness. However, the challenges of protocol fuzzing are the mutation of protocol messages and the deep interactivity of the protocol implementation. This paper proposes a model-based grey-box fuzzing approach for protocol implementations, including the server-side

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Yang, Yixiao. "Improve Model Testing by Integrating Bounded Model Checking and Coverage Guided Fuzzing." Electronics 12, no. 7 (2023): 1573. http://dx.doi.org/10.3390/electronics12071573.

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Eectromechanical systems built by Simulink or Ptolemy have been widely used in industry fields, such as autonomous systems and robotics. It is an urgent need to ensure the safety and security of those systems. Test case generation technologies are widely used to ensure the safety and security. State-of-the-art testing tools employ model-checking techniques or search-based methods to generate test cases. Traditional search-based techniques based on Simulink simulation are plagued by problems such as low speed and high overhead. Traditional model-checking techniques such as symbolic execution ha

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Zhang, Yifan, Kailong Zhu, Jie Peng, Yuliang Lu, Qian Chen, and Zixiong Li. "StatePre: A Large Language Model-Based State-Handling Method for Network Protocol Fuzzing." Electronics 14, no. 10 (2025): 1931. https://doi.org/10.3390/electronics14101931.

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As essential components for communication, network protocol programs are highly security-critical, making it crucial to identify their vulnerabilities. Fuzzing is one of the most popular software vulnerability discovery techniques, being highly efficient and having low false-positive rates. However, current network protocol fuzzing is hindered by the coarse-grained and missing state annotations in programs. The current solutions primarily rely on the manual modification of programs, which is inefficient and prone to omissions. In this paper, we propose StatePre, a novel state-handling method f

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Lin, Mingmin, Yingpei Zeng, and Yang Li. "RegFuzz: A Linear Regression-Based Approach for Seed Scheduling in Directed Fuzzing." Electronics 12, no. 17 (2023): 3650. http://dx.doi.org/10.3390/electronics12173650.

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Directed fuzzing aims to focus on fuzzing specific locations within a target program to enhance the efficiency of vulnerability discovery. However, directed fuzzing may yield fewer vulnerabilities and obtain lower code coverage when the specified locations have little to no vulnerabilities. Additionally, the existing directed fuzzing approaches often overlook the differences in variable values when calculating distances between seeds and specific locations. In order to address these issues, this paper introduces RegFuzz, a method that improves seed scheduling in directed fuzzing. RegFuzz utili

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Zhu, Xue Yong, and Zhi Yong Wu. "A New Fuzzing Technique Using Niche Genetic Algorithm." Advanced Materials Research 756-759 (September 2013): 4050–58. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.4050.

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Current advanced Fuzzing technique can only implement vulnerability mining on a single vulnerable statement each time, and this paper proposes a new multi-dimension Fuzzing technique, which uses niche genetic algorithm to generate test cases and can concurrently approach double vulnerable targets with the minimum cost on the two vulnerable statements each time. For that purpose, a corresponding mathematical model and the minimum cost theorem are presented. The results of the experiment show that the efficiency of the new proposed Fuzzing technique is much better than current advanced Fuzzing t

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Cheng, Mingjie, Kailong Zhu, Yuanchao Chen, Yuliang Lu, Chiyu Chen, and Jiayi Yu. "Reinforcement Learning-Based Multi-Phase Seed Scheduling for Network Protocol Fuzzing." Electronics 13, no. 24 (2024): 4962. https://doi.org/10.3390/electronics13244962.

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In network protocol fuzzing, effective seed scheduling plays a critical role in improving testing efficiency. Traditional state-driven seed scheduling methods in network protocol fuzzing are often limited by imbalanced seed selection, monolithic scheduling strategies, and ineffective power allocation. To overcome these limitations, we propose SCFuzz, specifically by employing a multi-armed bandit model to dynamically balance exploration and exploitation across multiple fuzzing phases. The fuzzing process is divided into initial, middle, and final phases with seed selection strategies adapted a

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Wang, Yunzhi, and Yufeng Li. "DCGFuzz: An Embedded Firmware Security Analysis Method with Dynamically Co-Directional Guidance Fuzzing." Electronics 13, no. 8 (2024): 1433. http://dx.doi.org/10.3390/electronics13081433.

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Microcontroller Units (MCUs) play a vital role in embedded devices due to their energy efficiency and scalability. The firmware in MCUs contains vulnerabilities that can lead to digital and physical harm. However, testing MCU firmware faces challenges due to various tool limitations and unavailable firmware details. To address this problem, research is turning to fuzzing and rehosting. Due to the inherent imbalance in computational resources of the fuzzing algorithm and the lack of consideration for the computational resource requirements of rehosting methods, some hardware behavior-related pa

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Xie, Yuchong, Wenhui Zhang, and Dongdong She. "ZTaint-Havoc: From Havoc Mode to Zero-Execution Fuzzing-Driven Taint Inference." Proceedings of the ACM on Software Engineering 2, ISSTA (2025): 917–39. https://doi.org/10.1145/3728916.

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Fuzzing is a popular software testing technique for discovering vulnerabilities. A central problem in fuzzing is identifying hot bytes that can influence program behavior. Taint analysis can track the data flow of hot bytes in a white-box fashion, but it often suffers from stability issues and cannot run on large real-world programs. Fuzzing-Driven Taint Inference (FTI) is a simple black-box technique to track hot bytes for fuzzing. It monitors the dynamic program behaviors of program execution instances and further infers hot bytes in a black-box fashion. However, this method requires additio

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Muduli, Sujit Kumar, and Subhajit Roy. "Satisfiability modulo fuzzing: a synergistic combination of SMT solving and fuzzing." Proceedings of the ACM on Programming Languages 6, OOPSLA2 (2022): 1236–63. http://dx.doi.org/10.1145/3563332.

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Programming languages and software engineering tools routinely encounter components that are difficult to reason on via formal techniques or whose formal semantics are not even available—third-party libraries, inline assembly code, SIMD instructions, system calls, calls to machine learning models, etc. However, often access to these components is available as input-output oracles—interfaces are available to query these components on certain inputs to receive the respective outputs. We refer to such functions as closed-box functions . Regular SMT solvers are unable to handle such closed-box fun

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Böhme, Marcel, Valentin J. M. Manès, and Sang Kil Cha. "Boosting Fuzzer Efficiency: An Information Theoretic Perspective." Communications of the ACM 66, no. 11 (2023): 89–97. http://dx.doi.org/10.1145/3611019.

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In this paper, we take the fundamental perspective of fuzzing as a learning process. Suppose before fuzzing, we know nothing about the behaviors of a program P : What does it do? Executing the first test input, we learn how P behaves for this input. Executing the next input, we either observe the same or discover a new behavior. As such, each execution reveals "some amount" of information about P 's behaviors. A classic measure of information is Shannon's entropy. Measuring entropy allows us to quantify how much is learned from each generated test input about the behaviors of the program. With

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Dissertationen zum Thema "Model fuzzing"

Duchene, Fabien. "Detection of web vulnerabilities via model inference assisted evolutionary fuzzing." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM022/document.

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Le test est une approche efficace pour détecter des bogues d'implémentation ayant un impact sur la sécurité, c.a.d. des vulnérabilités. Lorsque le code source n'est pas disponible, il est nécessaire d'utiliser des techniques de test en boîte noire. Nous nous intéressons au problème de détection automatique d'une classe de vulnérabilités (Cross Site Scripting alias XSS) dans les applications web dans un contexte de test en boîte noire. Nous proposons une approche pour inférer des modèles de telles applications et frelatons des séquences d'entrées générées à partir de ces modèles et d'une gramma

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Venger, Adam. "Black-box analýza zabezpečení Wi-Fi." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445533.

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Zariadenia, na ktoré sa každodenne spoliehame, sú stále zložitejšie a využívajú zložitejšie protokoly. Jedným z týchto protokolov je Wi-Fi. S rastúcou zložitosťou sa zvyšuje aj potenciál pre implementačné chyby. Táto práca skúma Wi-Fi protokol a použitie fuzz testingu pre generovanie semi-validných vstupov, ktoré by mohli odhaliť zraniteľné miesta v zariadeniach. Špeciálna pozornosť bola venovaná testovaniu Wi-Fi v systéme ESP32 a ESP32-S2. Výsledkom práce je fuzzer vhodný pre testovanie akéhokoľvek Wi-Fi zariadenia, monitorovací nástroj špeciálne pre ESP32 a sada testovacích programov pre ESP

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Ahmad, Abbas. "Model-Based Testing for IoT Systems : Methods and tools." Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCD008/document.

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L'internet des objets (IoT) est aujourd'hui un moyen d'innovation et de transformation pour de nombreuses entreprises. Les applications s'étendent à un grand nombre de domaines, tels que les villes intelligentes, les maisons intelligentes, la santé, etc. Le Groupe Gartner estime à 21 milliards le nombre d'objets connectés d'ici 2020. Le grand nombre d'objets connectés introduit des problèmes, tels que la conformité et l'interopérabilité en raison de l'hétérogénéité des protocoles de communication et de l'absence d'une norme mondialement acceptée. Le grand nombre d'utilisations introduit des pr

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Lone, Sang Fernand. "Protection des systèmes informatiques contre les attaques par entrées-sorties." Phd thesis, INSA de Toulouse, 2012. http://tel.archives-ouvertes.fr/tel-00863020.

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Les attaques ciblant les systèmes informatiques vont aujourd'hui au delà de simples logiciels malveillants et impliquent de plus en plus des composants matériels. Cette thèse s'intéresse à cette nouvelle classe d'attaques et traite, plus précisément, des attaques par entrées-sorties qui détournent des fonctionnalités légitimes du matériel, tels que les mécanismes entrées-sorties, à différentes fins malveillantes. L'objectif est d'étudier ces attaques, qui sont extrêmement difficiles à détecter par des techniques logicielles classiques (dans la mesure où leur mise en oeuvre ne nécessite pas l'i

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Korneva, Alexandrina. "The Cubicle Fuzzy Loop : A Testing Framework for Cubicle." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG095.

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L'objectif de cette thèse est d'intégrer une technique de test dans le model checker Cubicle. Pour cela, nous avons étendu Cubicle avec une boucle de Fuzzing (appelée Cubicle Fuzzy Loop - CFL). Cette nouvelle fonctionnalité remplit deux fonctions principales.Tout d'abord, elle sert d'oracle pour l'algorithme de génération d'invariants de Cubicle. Ce dernier, basé sur une exploration en avant de l'ensemble des états atteignables, était fortement limité par ses heuristiques lorsqu'elles sont appliquées à des modèles fortementconcurrents. CFL apporte une nouvelle manière plus efficace d'explorer

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Maggs, Kelly. "A Topological Model for Applications in Fuzzing." Master's thesis, 2020. http://hdl.handle.net/1885/209064.

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In this thesis, we introduce a topological model of dependencies motivated by applications in fuzzing. The model we define in Chapter 2 is framed within the language of finite topological spaces, partially ordered sets and simplicial complexes. In Chapter 3, we extend the theory to address situations where information is evolving dynamically and probabilistically, borrowing ideas from the general framework of persistence theory and Topological Data Analysis. In Chapter 4, we define algorithms to compute the relevant quantities and provide coarse bounds on their time complexity. Our model i

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Liao, Feng-Ze, and 廖峰澤. "Browser Fuzzing by Scheduled Mutation and Generation of Document Object Models." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/34522736060995439796.

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碩士<br>國立交通大學<br>網路工程研究所<br>103<br>Internet applications have made our daily life fruitful. However, they also cause many security problems if these applications are leveraged by intruders. Thus, it is important to find and fix vulnerabilities timely to prevent application vulnerabilities from being exploited. Fuzz testing is a popular methodology that effectively finds vulnerabilities in application programs with seed input mutation. However, it is not a satisfied solution for the web browsers. In this work, we propose a solution, called scheduled DOM fuzzing (SDF), which integrates several re

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Bücher zum Thema "Model fuzzing"

Wikert, Steven Micheal. Cherish me always: Teddy bears & warm fuzzies : antique photographs of children with stuffed animals. Hobby House Press, 2001.

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Wikert, Steven Micheal, and Mary McMurray Wikert. Teddy Bears & Warm Fuzzies: Antique Photographs of Children with Stuffed Animals. Hobby House Press, 2001.

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Buchteile zum Thema "Model fuzzing"

Sochor, Hannes, Flavio Ferrarotti, and Daniela Kaufmann. "Fuzzing-Based Grammar Inference." In Model and Data Engineering. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21595-7_6.

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Niemetz, Aina, Mathias Preiner, and Clark Barrett. "Murxla: A Modular and Highly Extensible API Fuzzer for SMT Solvers." In Computer Aided Verification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13188-2_5.

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AbstractSMT solvers are highly complex pieces of software with performance, robustness, and correctness as key requirements. Complementing traditional testing techniques for these solvers with randomized stress testing has been shown to be quite effective. Recent work has showcased the value of input fuzzing for finding issues, but this approach typically does not comprehensively test a solver’s API. Previous work on model-based API fuzzing was tailored to a single solver and a small subset of SMT-LIB. We present Murxla, a comprehensive, modular, and highly extensible model-based API fuzzer fo

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Muduli, Sujit Kumar, Rohan Ravikumar Padulkar, and Subhajit Roy. "Interactive Theorem Proving Modulo Fuzzing." In Computer Aided Verification. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65627-9_24.

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AbstractInteractive theorem provers (ITPs) exploit the collaboration between humans and computers, enabling proof of complex theorems. Further, ITPs allow extraction of provably correct implementations from proofs. However, often, the extracted code interface with external libraries containing real-life complexities—proprietary library calls, remote/cloud APIs, complex models like ML models, inline assembly, highly non-linear arithmetic, vector instructions etc. We refer to such functions/operations as closed-box components. For such components, the user has to provide appropriate assumed lemm

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Jonáš, Martin, Jan Strejček, Marek Trtík, and Lukáš Urban. "Gray-Box Fuzzing via Gradient Descent and Boolean Expression Coverage." In Tools and Algorithms for the Construction and Analysis of Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57256-2_5.

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AbstractWe present a gray-box fuzzing approach based on several new ideas. While standard gray-box fuzzing aims to cover all branches of the input program, our approach primarily aims to cover both results of each Boolean expression. To achieve this goal, we track the distances to flipping these results and we dynamically detect the input bytes that influence the distance. Then we use this information to efficiently flip the results. More precisely, we apply gradient descent on the detected bytes or we create new inputs by using detected bytes from different inputs.We implemented our approach

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Li, Xie, Zhaoyue Yuan, Zhenduo Zhang, Youcheng Sun, and Lijun Zhang. "Towards Large Language Model Guided Kernel Direct Fuzzing." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90900-9_2.

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Abstract Direct kernel fuzzing is a targeted approach that focuses on specific areas of the kernel, effectively addressing the challenges of frequent updates and the inherent complexity of operating systems, which are critical infrastructure. This paper introduces SyzAgent, a framework integrating LLMs with the state-of-the-art kernel fuzzer Syzkaller, where the LLMs are used to guide the mutation and generation of test cases in real-time. We present preliminary results demonstrating that this method is effective on around 67% cases in our benchmark during the experiment.

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Aldughaim, Mohannad, Kaled M. Alshmrany, Mikhail R. Gadelha, Rosiane de Freitas, and Lucas C. Cordeiro. "FuSeBMC_IA: Interval Analysis and Methods for Test Case Generation." In Fundamental Approaches to Software Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30826-0_18.

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AbstractThe cooperative verification of Bounded Model Checking and Fuzzing has proved to be one of the most effective techniques when testing C programs. FuSeBMC is a test-generation tool that employs BMC and Fuzzing to produce test cases. In Test-Comp 2023, we present an interval approach to FuSeBMC_IA, improving the test generator to use interval methods and abstract interpretation (via Frama-C) to strengthen our instrumentation and fuzzing. Here, an abstract interpretation engine instruments the program as follows. It analyzes different program branches, combines the conditions of each bran

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Chen, Chen, Zhouguo Chen, Yongle Hao, and Baojiang Cui. "Mocov: Model Based Fuzzing Through Coverage Guided Technology." In Advances on Broad-Band Wireless Computing, Communication and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69811-3_37.

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Chen, Yixiong, Yang Yang, Zhanyao Lei, Mingyuan Xia, and Zhengwei Qi. "Bootstrapping Automated Testing for RESTful Web Services." In Fundamental Approaches to Software Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71500-7_3.

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AbstractModern RESTful services expose RESTful APIs to integrate with diversified applications. Most RESTful API parameters are weakly typed, which greatly increases the possible input value space. This poses difficulties for automated testing tools to generate effective test cases to reveal web service defects related to parameter validation. We call this phenomenon the type collapse problem. To remedy this problem, we introduce FET (Format-encoded Type) techniques, including the FET, the FET lattice, and the FET inference to model fine-grained information for API parameters. Enhanced by FET

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Alshmrany, Kaled M., Mohannad Aldughaim, Ahmed Bhayat, and Lucas C. Cordeiro. "FuSeBMC v4: Smart Seed Generation for Hybrid Fuzzing." In Fundamental Approaches to Software Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99429-7_19.

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AbstractFuSeBMC is a test generator for finding security vulnerabilities in C programs. In Test-Comp 2021, we described a previous version that incrementally injected labels to guide Bounded Model Checking (BMC) and Evolutionary Fuzzing engines to produce test cases for code coverage and bug finding. This paper introduces an improved version of FuSeBMC that utilizes both engines to produce smart seeds. First, the engines run with a short time limit on a lightly instrumented version of the program to produce the seeds. The BMC engine is particularly useful in producing seeds that can pass throu

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Widl, Magdalena. "Test Case Generation by Grammar-Based Fuzzing for Model-Driven Engineering." In Hardware and Software: Verification and Testing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39611-3_28.

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Konferenzberichte zum Thema "Model fuzzing"

Wang, Jincheng, Le Yu, and Xiapu Luo. "LLMIF: Augmented Large Language Model for Fuzzing IoT Devices." In 2024 IEEE Symposium on Security and Privacy (SP). IEEE, 2024. http://dx.doi.org/10.1109/sp54263.2024.00211.

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Yu, Bo, Qihong Song, and Chengnuo Cai. "Large Language Model guided State Selection Approach for Fuzzing Network Protocol." In 2024 IEEE International Performance, Computing, and Communications Conference (IPCCC). IEEE, 2024. https://doi.org/10.1109/ipccc59868.2024.10850039.

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Lin, Wenfeng, Zhiyuan Jiang, Fangliang Xu, et al. "SVRM: Composing Various Network Service Fuzzing Corpus with One Single Model." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888546.

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Ezeobi, Uchenna, Sena Hounsinou, Habeeb Olufowobi, Yanyan Zhuang, and Gedare Bloom. "MCFICS: Model-based Coverage-guided Fuzzing for Industrial Control System Protocol Implementations." In IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2024. https://doi.org/10.1109/iecon55916.2024.10905724.

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Ammann, Max, Lucca Hirschi, and Steve Kremer. "DY Fuzzing: Formal Dolev-Yao Models Meet Cryptographic Protocol Fuzz Testing." In 2024 IEEE Symposium on Security and Privacy (SP). IEEE, 2024. http://dx.doi.org/10.1109/sp54263.2024.00096.

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Borcherding, Anne, Mark Giraud, and Johannes Häring. "Bringing Light into the Darkness: Leveraging Hidden Markov Models for Blackbox Fuzzing." In 2025 IEEE/ACM International Conference on Automation of Software Test (AST). IEEE, 2025. https://doi.org/10.1109/ast66626.2025.00021.

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Goel, Aman, Xian Wu, Zhe Wang, Dmitriy Bespalov, and Yanjun Qi. "TurboFuzzLLM: Turbocharging Mutation-based Fuzzing for Effectively Jailbreaking Large Language Models in Practice." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-industry.43.

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Zhang, Kunpeng, Shuai Wang, Jitao Han, et al. "Your Fix Is My Exploit: Enabling Comprehensive DL Library API Fuzzing with Large Language Models." In 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE). IEEE, 2025. https://doi.org/10.1109/icse55347.2025.00041.

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Nakanishi, Hibiki, Kento Hasegawa, Seira Hidano, Kazuhide Fukushima, Kazuo Hashimoto, and Nozomu Togawa. "Performance Comparison of the LLM Models on LLM-Based Seed Generation Method for IoT Device Fuzzing." In 2025 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2025. https://doi.org/10.1109/icce63647.2025.10929957.

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Gubbi, Kevin Immanuel, Mohammadnavid Tarighat, Arvind Sudarshan, et al. "State of Hardware Fuzzing: Current Methods and the Potential of Machine Learning and Large Language Models." In 2025 26th International Symposium on Quality Electronic Design (ISQED). IEEE, 2025. https://doi.org/10.1109/isqed65160.2025.11014308.

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