Large Language Models Based Fuzzing Techniques: A Survey
(2402.00350)Abstract
In the modern era where software plays a pivotal role, software security and vulnerability analysis have become essential for software development. Fuzzing test, as an efficient software testing method, are widely used in various domains. Moreover, the rapid development of LLMs has facilitated their application in the field of software testing, demonstrating remarkable performance. Considering that existing fuzzing test techniques are not entirely automated and software vulnerabilities continue to evolve, there is a growing trend towards employing fuzzing test generated based on LLMs. This survey provides a systematic overview of the approaches that fuse LLMs and fuzzing tests for software testing. In this paper, a statistical analysis and discussion of the literature in three areas, namely LLMs, fuzzing test, and fuzzing test generated based on LLMs, are conducted by summarising the state-of-the-art methods up until 2024. Our survey also investigates the potential for widespread deployment and application of fuzzing test techniques generated by LLMs in the future.
Overview
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The paper discusses the integration of LLMs like CodeX and InCoder into fuzzing methodologies to enhance software testing efficiency and accuracy.
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It provides a comprehensive analysis of LLMs-based fuzzing, evaluating the systems across code, performance, and time-related metrics to highlight their advantages over traditional methods.
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Applications and methodologies of LLMs-based fuzzing for AI and non-AI software testing are explored, demonstrating the versatility and improved outcomes of these systems.
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Future directions include developing specialized LLMs-based fuzzers informed by historical bug datasets, while highlighting challenges such as dataset quality and code generation time.
LLMs Enhancing Fuzzing Test Techniques for Software Security
Introduction to LLMs-based Fuzzers
The integration of LLMs into fuzzing test methodologies represents a significant advancement in software testing, offering increased efficiency and accuracy. As software vulnerabilities evolve, there's a palpable shift towards leveraging these advanced models for generating fuzzing tests. LLMs like CodeX and InCoder have been foundational in developing new systems, notably enhancing automated software testing processes. This progression underlines the relevance of fusing LLMs with fuzzing to meet the modern software industry's demands.
The State of LLMs-based Fuzzing
The survey captures a comprehensive analysis across three critical areas: the utilization of LLMs in fuzzing, the comparative advantages of LLMs-based fuzzers over traditional methods, and the potential future of LLMs-based fuzzing technology. Through a meticulous literature review, supplemented by statistical analyses, insights into the advancement of fuzzing tests are discussed. Specifically, prominent methodologies like TitanFuzz and FuzzGpt are highlighted for their innovative approaches, marrying different LLMs with fuzzing techniques to pioneer new testing systems.
Evaluating LLMs-based Fuzzers
A significant part of the survey explore the metrics used for evaluating LLMs-based fuzzers, categorizing them into code-related, performance-related, and time-related metrics. This classification provides a nuanced understanding of how these systems are assessed, focusing on code coverage, bug retrieval effectiveness, hit rate, mutation effectiveness, and detection time. Such metrics serve not only to benchmark LLMs-based fuzzers but also to clarify their distinct advantages over conventional fuzzing tests, including the generation of more diverse and complex test cases and improved detection of subtle vulnerabilities.
Applications in AI and Non-AI Software Testing
The differentiation between LLMs-based fuzzing applications in AI versus non-AI software illuminates diverse methodologies tailored to software types. For AI software, the survey reveals techniques that exploit LLMs for prompt engineering and mutation, enhancing the precision of fuzzing tests. Contrarily, non-AI software fuzzing leverages LLMs for their robust code generation capacities, addressing the need for universal testing frameworks capable of handling varied programming languages.
Advantages Over Traditional Fuzzing
Comparative analysis underscores several areas where LLMs-based fuzzers outperform traditional methods, including superior code coverage, computational efficiency, and the ability to unearth more complex errors. These advantages are illustrated through examples like TitanFuzz and FuzzGpt, showcasing notable improvements in API coverage and the detection of previously unknown vulnerabilities.
Future Directions and Challenges
The discussion on future work emphasizes the evolution towards developing specialized LLMs-based fuzzers informed by historical bug datasets. This approach is contrasted with the contemporary strategy of integrating LLMs into existing fuzzing frameworks. Moreover, the survey speculates on challenges such as dataset quality, time consumption in code generation, and the necessity for a comprehensive evaluation framework tailored to LLMs-based fuzzing tests.
Conclusion
LLMs-based fuzzing test technology represents a monumental stride in software security, demonstrating marked improvements in efficiency, accuracy, and automation over traditional fuzzing methodologies. The survey positions this integration as a pivotal development, promising not only to refine the scope of software testing but also to catalyze the next wave of innovation in cybersecurity practices. As this field continues to evolve, the breadth of LLM applications in software testing is expected to expand, further solidifying the role of these models in advancing the domain.
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