CAREER: Fuzzing Large Software: Principles, Methods, and Tools

职业：模糊大型软件：原理、方法和工具

• 批准号: 2340198
• 负责人: Jun Xu
• 金额: $ 55.55万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2029-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340198&HistoricalAwards=false
• 关键词: CAREER; Fuzzing; Large; Software; Principles

Today's software is reaching unprecedented sizes, resulting in a variety of large software systems, such as web browsers, email clients, and database systems, occupying a central role in society. However, this surge in size brings forth a myriad of vulnerabilities that threaten everyone's digital security. Statistics reveal that software exceeding one million lines of code harbors an average of 0.66 defects per 1,000 lines, with 36% classified as vulnerabilities. This project addresses this critical issue by exploring security testing tailored explicitly for discovering vulnerabilities in large software. It focuses on scaling fuzzing---a predominant testing strategy embraced by software vendors and open-source communities---to maintain high effectiveness and efficiency for large software. The research outcomes advance the scientific study of security testing under new challenges posed by large software's unique properties. The anticipated results also improve the security of various types of large software that play a crucial role in daily lives, such as Chromium, Firefox, Thunderbird, MySQL, LibreOffice, PDFium, TensorFlow, and OpenCV. The outcomes of the research will lead to technology transfer to industry. The research will be integrated into education and training through new curriculum and outreach to Utah's Youth Education program as well as capture the flag (CTF) competitions.Technically, this project introduces three key innovations to enable scalable fuzzing for large software. First, it employs object-oriented decomposition to address the extreme complexity of large software, breaking it down into self-contained code units based on the data objects it manipulates. This approach allows for testing individual code units, overcoming the challenges associated with fuzzing entire software systems and enabling deeper code coverage. Second, the project integrates fuzzing-centric optimizations into compilers and operating systems to enhance testing speed. These optimizations minimize fuzzing-irrelevant operations and dynamically adapt to the progress of fuzzing, unlocking hidden speed potential. Third, the project develops history-informed crash analysis to expedite the fuzzing-to-patching cycle by filtering and triaging crashes encountered during testing. Leveraging historical data produced by fuzzing, this analysis comprehensively understands and processes crashes, offering the fidelity and efficiency necessary for addressing large software systems.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.

当今的软件正在达到前所未有的大小，从而产生了各种大型软件系统，例如Web浏览器，电子邮件客户端和数据库系统，在社会中占据了中心作用。但是，这种大小的激增带来了无数的脆弱性，威胁到每个人的数字安全。统计数据显示，超过一百万行代码的软件平均每1,000行的缺陷为0.66，其中36％归类为漏洞。该项目通过探索针对大型软件中发现漏洞的明确量身定制的安全测试来解决这一关键问题。它着重于扩展模糊性---软件供应商和开源社区所接受的主要测试策略 - 以保持大型软件的高效和效率。研究结果推进了大软件独特属性带来的新挑战的安全测试的科学研究。预期的结果还提高了各种类型的大软件的安全性，这些软件在日常生活中起着至关重要的作用，例如铬，Firefox，Thunderbird，Mysql，Libreoffice，libreoffice，pdfium，Tensorflow和OpenCV。该研究的结果将导致技术转移到行业。这项研究将通过新的课程和犹他州的青年教育计划纳入教育和培训，并捕获Flag（CTF）竞​​赛。技术上，该项目介绍了三个关键的创新，以启用大型软件的可扩展模糊性。首先，它采用面向对象的分解来解决大型软件的极端复杂性，并根据其操纵的数据对象将其分解为独立的代码单元。这种方法允许测试各个代码单元，克服与整个软件系统相关的挑战，并实现更深入的代码覆盖范围。其次，该项目将以模糊为中心的优化整合到编译器和操作系统中，以提高测试速度。这些优化最大程度地减少了模糊含量的操作，并动态适应了模糊的进度，解锁了隐藏的速度电位。第三，该项目通过过滤和测试过程中遇到的过滤和分类崩溃来开发历史知识的崩溃分析，以加快构图对围周期。该分析利用模糊产生的历史数据，全面理解和流程崩溃，提供解决大型软件系统所必需的保真度和效率。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准通过评估来获得支持的。