CAREER: System Techniques to Improve Fuzzing Performance

模糊是一种自动软件测试技术，它重复地将随机变异的输入注入目标程序。事实证明，Fuzing在查找复杂的真实程序中的错误方面很有效，它已经成为查找安全漏洞的核心技术。现在有大公司构建大规模分布式模糊基础设施的例子，该基础设施运行在数百台虚拟机上，每天无情地处理超过数百万个测试用例。模糊器的性能是至关重要的，因为更快、更智能的模糊器将更快地发现目标程序中的更多安全漏洞。该项目采用了一种新颖的方法来模糊性能，通过在固定的时间间隔内实现更多的测试覆盖来缩短每次模糊迭代的执行时间，而其他方法则专注于收敛到更可能触发漏洞的输入集。在此过程中，该项目将克服操作系统和系统管理程序等系统软件层造成的隐藏的可扩展性和性能瓶颈。这样的技术进步可以显著节省模糊化基础设施的运营成本，并帮助开发人员以经济高效的方式识别开放源代码和商业软件中的更多安全漏洞。该奖项反映了NSF的法定使命，并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。

Fuzzing is an automatic software-testing technique that repeatedly injects a randomly mutated input to a target program. Proven to be effective in finding bugs in complex, real-world programs, fuzzing has become a core technique for finding security vulnerabilities. There are now examples of major companies building large-scale, distributed fuzzing infrastructure, which runs on hundreds of virtual machines that relentlessly process over millions of test cases per day. The performance of fuzzers is critical, as a faster, smarter fuzzer will find more security bugs in the target program more quickly.This project takes a novel approach to fuzzing performance by shortening the execution time of each fuzzing iteration by trying to achieve more test coverage in a fixed time interval, whereas other approaches focus on convergence to input sets that are more likely to trigger a vulnerability. In the process, the project will overcome hidden scalability and performance bottlenecks caused at the system software layers, such as operating system and hypervisor. Such technical advances can bring significant saving of the operation cost of fuzzing infrastructure and help developers to identify more security bugs in open source and commercial software in a cost-effective manner.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.