CAREER: System Techniques to Improve Fuzzing Performance

Fuzzing是一种自动软件测试技术，它重复地向目标程序注入随机变异的输入。为了有效地发现复杂的真实程序中的错误，模糊技术已经成为发现安全漏洞的核心技术。现在有一些大公司构建大规模分布式模糊基础设施的例子，这些基础设施运行在数百台虚拟机上，每天无情地处理数百万个测试用例。模糊器的性能是至关重要的，因为一个更快，更聪明的模糊器将更快地发现目标程序中更多的安全漏洞。该项目采取了一种新的方法来模糊性能，通过缩短每个模糊迭代的执行时间，试图在固定的时间间隔内实现更多的测试覆盖率，而其他方法则专注于收敛到更有可能触发漏洞的输入集。在此过程中，该项目将克服在系统软件层（如操作系统和虚拟机管理程序）造成的隐藏的可扩展性和性能瓶颈。这类技术进步可以大大节省基础设施的运营成本，并帮助开发人员以具有成本效益的方式识别开源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。

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.