Grammar-Based Fuzzing

Sargsyan, Sevak; Kurmangaleev, Shamil; Mehrabyan, Matevos; Мишечкин, Максим Владимирович; Ghukasyan, Tsolak; Asryan, Sergey

doi:10.1109/ivmem.2018.00013

Cited by 15 publications

(3 citation statements)

References 3 publications

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“…Their proposed prototype uses random selection to determine what paths to traverse across the generation process. The authors Sargsyan et al improved upon this [15] and were able to implement a weight-based system to help control the traversal process. One advantage of our contribution over Sargsyan et al [15] is our proposed implementation to deal with recursive mitigation controls.…”

Section: B Grammar Assisted Sentence Generationmentioning

confidence: 99%

Enabling Generative AI to Produce SQL Statements: A Framework for the Auto- Generation of Knowledge Based on EBNF Context-Free Grammars

Troy,

Sturley,

Alcaraz-Calero

et al. 2023

IEEE Access

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The motivation of this paper is to be able to generate high-quality (Structured Query Language) SQL language sentences in terms of syntax and semantics so that they are intended to achieve a concrete predefined and well-known aim. For example, generating SQL-sentences that are capable of detecting a cyber-attack from a set of metrics available in a database table. Two solutions are needed to achieve so, a tool that enables and performs the syntactically valid generation of SQL sentences and an (Artificial intelligence) AI algorithm able to guide the semantics of such generations to the achievement of the best sentences for the intended purpose. The main contribution of this manuscript is the first of these solutions. To be concrete, this paper proposes a tool to enable and generate syntactic-valid language sentences. The tool can deal with any language defined as an ANTLR4 EBNF (Extended Backus-Naur Form) grammar. The paper also provides a methodology to help achieve an EBNF grammar suitable for addressing concerns related to ambiguity and recursion as a direct result of the generation process. The paper further implements a prototype utilizing ANTLR4's recognizer and its Augmented Transition Network for language generation using EBNF grammars. In-depth design and logic implementation are provided, showcasing areas of interest for AI integration. The achieved prototype showed an ability to easily generate syntactically valid SQL statements at various depths, with observable problems becoming more apparent during the exponential recursive growth. Our mitigation controls for such scenarios proved to be successful and were able to complete the recursion whilst also moving the push-down automata forward until query completion. Experimental validation was performed against a SQL EBNF grammar feeding the generated SQL statement into an SQL parser to validate the syntax.

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Section: B Grammar Assisted Sentence Generationmentioning

confidence: 99%

Enabling Generative AI to Produce SQL Statements: A Framework for the Auto- Generation of Knowledge Based on EBNF Context-Free Grammars

Troy,

Sturley,

Alcaraz-Calero

et al. 2023

IEEE Access

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show abstract

“…It plays an important role in the process of functionality/security testing of network communication software or hardware equipment [2,3]. For example, when conducting fuzzing on network communication software, network protocol syntax information ensures the validity of generated test cases, thereby enhancing fuzzing efficiency [4][5][6]. However, the complexity and diversity of network protocols present significant challenges to understand and process them thoroughly.…”

Section: Introductionmentioning

confidence: 99%

A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing

Li,

Zhang,

Zhao

et al. 2024

Applied Sciences

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Network protocol syntax information plays a crucial role in grammar-based fuzzing. Current network protocol syntax extraction methods are less versatile, inefficient, and the extracted information is not comprehensive. This paper proposes a novel method for extracting syntax information, which innovatively extracts network protocol syntax from Wireshark protocol dissector files. The extracted syntax information includes packet types of the protocol, the constituent fields of each packet type, and detailed attributes of each field. Based on this method, an automated system for network protocol syntax information extraction was developed. The experiment was conducted with this system on a variety of protocols including DCCP, DNP3.0, Modbus TCP, and S7COMM. The experimental results show that compared with the current methods, our method has a better performance in terms of efficiency and versatility and at the same time ensures the comprehensiveness and accuracy of the extracted syntax information.

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“…In order to achieve extendibility of the framework, ISP-Fuzzer provides opportunity to add custom plugins for different tasks solution. It contains number of implemented plugins, such as: BNF data generation plugin [17,18], directed fuzzing plugin [19], DSE invocation plugin [20], etc. ISP-Fuzzer has its own mutation engine with several mutation algorithms.…”

Section: Introductionmentioning

confidence: 99%

Improving fuzzing performance by applying interval mutations

Саргсян¹,

Акопян²,

Мовсисян³

et al. 2019

Proceedings of ISP RAS

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This paper presents a novel approach of generation effective inputs for fuzz testing. Most applications check input format before performing basic calculations. That kind of applications usually parse service information of input file to decide whether it is supported or not. Input formats which are not supported are discarded and the application finishes its execution immediately. For example, the service information of ELF (Extensible Linking Format) file should start with the following data: "0x7f 'E' 'L' 'F'". If a file does not contain this information in header section then it will not be considered as ELF. Effective fuzzing of an application which has input validation stage is a relevant and important problem. Random changes of input files usually malform service data and the target application finishes immediately without execution of main code. This makes fuzzing process inefficient. To solve this problem, we have designed and implemented three special plugins for ISP-Fuzzer. The first plugin is intended to collect execution traces. The second plugin connects fragments of input data and executed basic blocks of the target program. Based on that information we can determine potential fragments (critical fragments) of input data which should not be mutated for new test case generation. The third plugin is designed for interval mutations. It mutates input file escaping critical fragments detected by the second plugin. Experimental results prove the effectiveness of proposed method.

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Grammar-Based Fuzzing

Cited by 15 publications

References 3 publications

Enabling Generative AI to Produce SQL Statements: A Framework for the Auto- Generation of Knowledge Based on EBNF Context-Free Grammars

Enabling Generative AI to Produce SQL Statements: A Framework for the Auto- Generation of Knowledge Based on EBNF Context-Free Grammars

A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing

Improving fuzzing performance by applying interval mutations

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