GSA-Fuzz: Optimize Seed Mutation with Gravitational Search Algorithm

Lin, Mingmin; Zeng, Yingpei; Wu, Ting; Wang, Qiuhua; Fang, Linan; Guo, Shanqing

doi:10.1155/2022/1505842

Cited by 3 publications

(3 citation statements)

References 40 publications

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“…DAFuzz follows the same approach. CGF fuzzers have successfully been used to discover many vulnerabilities ( Zalewski, 2017 ; Google Security Team, 2018 ), and become popular in both the security industry ( Zalewski, 2017 ; Honggfuzz, 2023 ; libFuzzer, 2023 ; Fioraldi et al, 2020 ; Google Security Team, 2018 ) and academia ( Böhme, Pham & Roychoudhury, 2016 ; Lemieux & Sen, 2018 ; Gan et al, 2018 ; Lyu et al, 2019 ; Pham, Böhme & Roychoudhury, 2020 ; Aschermann et al, 2020 ; Yue et al, 2020 ; Wang, Song & Yin, 2021 ; Lin et al, 2022 ; Fioraldi, Maier & Balzarotti, 2022 ).…”

Section: Related Workmentioning

confidence: 99%

DAFuzz: data-aware fuzzing of in-memory data stores

Zeng,

Zhu,

Zhang

et al. 2023

PeerJ Computer Science

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Fuzzing has become an important method for finding vulnerabilities in software. For fuzzing programs expecting structural inputs, syntactic- and semantic-aware fuzzing approaches have been particularly proposed. However, they still cannot fuzz in-memory data stores sufficiently, since some code paths are only executed when the required data are available. In this article, we propose a data-aware fuzzing method, DAFuzz, which is designed by considering the data used during fuzzing. Specifically, to ensure different data-sensitive code paths are exercised, DAFuzz first loads different kinds of data into the stores before feeding fuzzing inputs. Then, when generating inputs, DAFuzz ensures the generated inputs are not only syntactically and semantically valid but also use the data correctly. We implement a prototype of DAFuzz based on Superion and use it to fuzz Redis and Memcached. Experiments show that DAFuzz covers 13~95% more edges than AFL, Superion, AFL++, and AFLNet, and discovers vulnerabilities over 2.7× faster. In total, we discovered four new vulnerabilities in Redis and Memcached. All the vulnerabilities were reported to developers and have been acknowledged and fixed.

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Section: Related Workmentioning

confidence: 99%

DAFuzz: data-aware fuzzing of in-memory data stores

Zeng,

Zhu,

Zhang

et al. 2023

PeerJ Computer Science

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“…Usually, researchers enhance fuzzing techniques by focusing on optimizing either the seed mutation strategy or the seed selection strategy. In the context of seed mutation strategy optimization, several approaches have been explored, such as those presented in [4,12,[23][24][25][26][27][28][29]. These methods aim to identify the most effective mutation techniques and the appropriate positions for seed mutations during the fuzzing process.…”

Section: General Fuzzingmentioning

confidence: 99%

RegFuzz: A Linear Regression-Based Approach for Seed Scheduling in Directed Fuzzing

Lin,

Zeng,

2023

Electronics

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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 utilizes a linear regression model to predict the effectiveness of a seed and allocates more fuzzing opportunities to efficient seeds. Specifically, first, RegFuzz defines several labels with the corresponding trainable weights for each seed. These labels encompass seed coverage, crash efficiency, seed distance, and more. In the calculation of seed distance, RegFuzz takes into account not only the basic block distance but also the variable distance contained within those basic blocks. Second, the linear regression model continually optimizes the label weights during fuzzing, and these optimized weights are employed to predict the effectiveness of seeds. In comparison with AFLGo, AFL, and AFL++, RegFuzz demonstrates higher code coverage and a more efficient bug-finding capability across seven real-world open-source programs.

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“…In the context of security testing, MAS can enable coordinated efforts among agents to explore different parts of the software system simultaneously, leading to a more comprehensive vulnerability detection [8]. By leveraging MAS in DRL fuzzing, researchers aim to improve the scalability and efficiency of security testing processes [9]. This review aims to provide a comprehensive overview of the advancements in smart contract security through the integration of multi-agent DRL fuzzing techniques.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Smart Contract Security Through Multi-Agent Deep Reinforcement Learning Fuzzing: A Survey of Approaches and Techniques

Andrijasa,

Ismail,

Ahmad

et al. 2024

ijacsa

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Multi-Agent Systems (MAS) and Deep Reinforcement Learning (DRL) have emerged as powerful tools for enhancing security measures, particularly in the context of smart contract security in blockchain technology. This literature review explores the integration of Multi-Agent DRL fuzzing techniques to bolster the security of smart contracts. The study delves into the formalization of emergence in MAS, the comprehensive survey of multi-agent reinforcement learning, and progress on the state explosion problem in model checking. By addressing challenges such as state space explosion, real-time detection, and adaptability across blockchain platforms, researchers aim to advance the field of smart contract security. The review emphasizes the significance of Multi-Agent DRL fuzzing in improving security testing processes and calls for future research and collaboration to enhance the resilience and integrity of decentralized applications. Through advancements in algorithmic efficiency, the incorporation of Explainable AI, crossdomain applications of MAS, and cooperation with blockchain development teams, the future of smart contract security holds promise for robust and secure blockchain ecosystems.

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GSA-Fuzz: Optimize Seed Mutation with Gravitational Search Algorithm

Cited by 3 publications

References 40 publications

DAFuzz: data-aware fuzzing of in-memory data stores

DAFuzz: data-aware fuzzing of in-memory data stores

RegFuzz: A Linear Regression-Based Approach for Seed Scheduling in Directed Fuzzing

Enhancing Smart Contract Security Through Multi-Agent Deep Reinforcement Learning Fuzzing: A Survey of Approaches and Techniques

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