SaTC: CORE: Small: Machine Learning for Effective Fuzz Testing

SaTC：核心：小型：用于有效模糊测试的机器学习

• 批准号: 1817122
• 负责人: Koushik Sen
• 金额: $ 50万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1817122&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; Machine; Learning

In recent years, fuzz testing has evolved as one of the most effective testing techniques for finding security vulnerabilities and correctness bugs in real-world software systems. It has been used successfully by major software companies for security testing and quality assurance. State-of-the-art fuzz testing tools have found numerous security vulnerabilities and bugs in widely used software such as Web browsers, network tools, image processors, popular system libraries, C compilers, and interpreters.Fuzz testing works by generating random input data for a program under test. A key reason behind its huge popularity is that it has low computation overhead compared to other sophisticated techniques such as dynamic symbolic execution. While fuzz testing has been highly successful in practice, it has been mostly implemented in ad-hoc ways by incorporating a collection of hacks and best practices. As such, fuzz testing techniques usually generate many redundant test inputs and take several days to weeks to find bugs. For complex input formats, such as for random C program inputs for a C compiler, a huge amount of manual tuning is required to make fuzz testing generate valid test inputs. This project proposes to make fuzz testing smarter and more effective by applying machine learning with customizable testing objectives. The proposed techniques will use probabilistic machine learning models, such as n-grams, recurrent neural networks (RNN), recursive neural networks, or multi-armed bandits, to generate inputs from scratch or to mutate a set of seed inputs. The model will be trained in such a way that the inputs generated by it will maximize the custom testing objective. We expect that such a model will generate fewer redundant inputs and can be customized to user-provided testing objectives. This project aims to contribute to the development of reliable, secure, and trustworthy software. The tools and techniques developed in this project will make it easier for programmers to write correct and secure programs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

近年来，模糊测试已经发展成为在真实世界的软件系统中发现安全漏洞和正确性错误的最有效的测试技术之一。 它已成功地用于主要软件公司的安全测试和质量保证。 最先进的模糊测试工具已经在广泛使用的软件中发现了许多安全漏洞和错误，例如Web浏览器，网络工具，图像处理器，流行的系统库，C编译器和解释器。 其大受欢迎的一个关键原因是，与其他复杂的技术（如动态符号执行）相比，它的计算开销较低。 虽然模糊测试在实践中非常成功，但它主要是通过整合黑客和最佳实践的集合以临时方式实现的。因此，模糊测试技术通常会产生许多冗余的测试输入，并需要几天到几周的时间来找到bug。对于复杂的输入格式，例如C编译器的随机C程序输入，需要大量的手动调优才能使模糊测试生成有效的测试输入。 该项目提出通过应用具有可定制测试目标的机器学习来使模糊测试更智能，更有效。所提出的技术将使用概率机器学习模型，如n-gram，递归神经网络（RNN），递归神经网络或多臂强盗，从头开始生成输入或变异一组种子输入。 该模型将以这样一种方式进行训练，即它生成的输入将最大限度地提高自定义测试目标。我们希望这样的模型将产生更少的冗余输入，并可以定制用户提供的测试目标。该项目旨在为开发可靠、安全和值得信赖的软件做出贡献。 该项目开发的工具和技术将使程序员更容易编写正确和安全的程序。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

JQF: coverage-guided property-based testing in Java

• DOI: 10.1145/3293882.3339002
• 发表时间: 2019-07
• 期刊: Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis
• 作者: Rohan Padhye;Caroline Lemieux;Koushik Sen

FUZZFACTORY: Domain-Specific Fuzzing with Waypoints

• DOI: 10.1145/3360600
• 发表时间: 2019-10-01
• 期刊: PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL
• 影响因子: 1.8
• 作者: Padhye, Rohan;Lemieux, Caroline;Vijayakumar, Hayawardh
• 通讯作者: Vijayakumar, Hayawardh

Quickly Generating Diverse Valid Test Inputs with Reinforcement Learning ICSE 2020

使用强化学习快速生成多样化的有效测试输入 ICSE 2020

• DOI: 10.1145/3380399
• 发表时间: 2020
• 期刊: International conference on software engineering (ICSE'2020
• 作者: Sameer Reddy, Caroline Lemieux

PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion

• DOI: 10.48550/arxiv.2210.13715
• 发表时间: 2022-10
• 期刊: ArXiv
• 作者: Jianhao Shen;Chenguang Wang;Ye Yuan;Jiawei Han;Heng Ji;Koushik Sen;Ming Zhang;Dawn Song

Benchmarking Language Models for Code Syntax Understanding

• DOI: 10.48550/arxiv.2210.14473
• 发表时间: 2022-10
• 作者: Da Shen;Xinyun Chen;Chenguang Wang;Koushik Sen;Dawn Song