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.

今天的软件正达到前所未有的规模，导致各种大型软件系统，如Web浏览器，电子邮件客户端和数据库系统，在社会中占据中心地位。然而，这种规模的激增带来了无数的漏洞，威胁到每个人的数字安全。统计数据显示，超过100万行代码的软件平均每1,000行包含0.66个缺陷，其中36%被归类为漏洞。这个项目通过探索为发现大型软件中的漏洞而专门定制的安全测试来解决这个关键问题。它专注于扩展模糊测试--软件供应商和开源社区所采用的主要测试策略--以保持大型软件的高效率和高效率。研究成果推进了大型软件独特属性所带来的新挑战下的安全测试科学研究。预期的结果还提高了在日常生活中发挥关键作用的各种大型软件的安全性，例如Chromium，Firefox，Thunderbird，MySQL，LibreOffice，PDFium，TensorFlow和OpenCV。研究成果将导致向工业界转让技术。这项研究将通过新的课程和外展到犹他州的青年教育计划以及捕捉国旗（CTF）competitions.Technically，该项目引入了三个关键的创新，使可扩展的模糊大型软件的教育和培训。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，根据它操作的数据对象将其分解为自包含的代码单元。这种方法允许测试单个代码单元，克服与模糊整个软件系统相关的挑战，并实现更深的代码覆盖率。其次，该项目将以模糊为中心的优化集成到编译器和操作系统中，以提高测试速度。这些优化最大限度地减少了与模糊无关的操作，并动态适应模糊的进展，释放隐藏的速度潜力。第三，该项目开发了历史信息崩溃分析，通过过滤和分类测试期间遇到的崩溃来加快模糊修补周期。该分析充分利用模糊化产生的历史数据，全面了解和处理崩溃，为解决大型软件系统提供必要的保真度和效率。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。