SaTC: CORE: Small: Collaborative: Towards Facilitating Kernel Vulnerability Reproduction by Fusing Crowd and Machine Generated Data

内核是计算机操作系统中的核心软件。由于内核软件的高度复杂性，在开发阶段找到所有漏洞几乎是不可能的。近年来，众包努力在发现内核漏洞方面取得了巨大成功，安全专业人员、黑客和用户都可以通过提交内核漏洞报告来做出贡献。然而，研究表明，许多漏洞报告，包括那些由自动化工具(如内核模糊器)生成的报告，不易重现。不可重现的报告可能会导致修补过程出现重大延迟，或导致内核供应商误判漏洞的严重性。初步研究表明，漏洞报告不可重现，原因是1)缺少有关编译配置的信息；(2)缺乏构建触发漏洞的上下文的数据；以及(3)有关易受攻击的内核版本的信息不准确或不完整。这个项目将开发结合群众报告和机器生成的数据以及静态-动态程序分析的新方法，以自动化推断、构建和验证内核漏洞复制所需的信息的过程。该项目将为复制内核错误和漏洞提供急需的自动化。如果成功，该项目将大大提高计算机安全(用于内核漏洞分析)，并为软件工程领域(用于错误诊断和评估)做出贡献。通过提高内核错误的繁殖率，该项目还将帮助其他并行的漏洞修补和补救工作。预期的进步是三倍的。(1)团队将开发新的推理方法，根据内存快照和错误报告中的代码段来推断内核编译配置。它将设计新的方法来处理由错误导致的不受信任或损坏的内存转储。(2)团队成员将开发新的机制来构建准确的触发上下文，以触发报告的错误(通过内核故障操作和注入)。该上下文构建方法还能够准确定位相关的错误进程，并正确处理内核中断。(3)新的模糊工具将设计用于迁移输入程序，以实现跨内核版本的更广泛的错误测试，以及快速确定非易受攻击的版本的新方法。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The kernel is the core piece of software in a computer's operating system. Due to the high complexity of kernel software, finding all vulnerabilities during the development phase is nearly impossible. In recent years, crowdsourcing efforts have shown great success in discovering kernel vulnerabilities, where security professionals, hackers, and users can all contribute by submitting kernel bug reports. However, research shows that many vulnerability reports, including those generated by automated tools (e.g., kernel fuzzers), are not easily reproducible. Non-reproducible reports can cause significant delays to the patching process or lead kernel vendors to misjudge the severity of the vulnerability. Preliminary research shows vulnerability reports are not reproducible due to 1) missing information on the compilation configuration; (2) a lack of data to construct the contexts for triggering the bug; and (3) inaccurate or incomplete information about the vulnerable kernel versions. This project will develop new approaches combining crowd-reported and machine-generated data and static-dynamic program analysis to automate the process of inferring, constructing, and validating the needed information for kernel-vulnerability reproduction.This project will provide much-needed automation for reproducing kernel bugs and vulnerabilities. If successful, the project will significantly advance computer security (for kernel vulnerability analysis) and contribute to the field of software engineering (for bug diagnosis and assessment). By improving the reproduction rate of kernel bugs, this project will also help with other parallel efforts for vulnerability patching and remediation. The expected advancements are three-fold. (1) The team will develop novel inference methods to infer the kernel compilation configuration based on memory snapshots and code segments in the bug reports. It will design new approaches to handle the untrusted or corrupted memory dumps caused by the bugs. (2) Team members will develop new mechanisms to construct precise triggering contexts to trigger the reported bugs (via kernel fault manipulation and injection). The context construction method is also able to pinpoint relevant faulty processes and handle kernel interrupt correctly. (3) New fuzzing tools will be designed to migrate input programs to enable much broader bug testing across kernel versions, and new methods to quickly determine non-vulnerable versions.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.