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Fw-fuzz: A code coverage-guided fuzzing framework for network protocols on firmware
Concurrency and Computation: Practice and Experience ( IF 1.5 ) Pub Date : 2020-04-08 , DOI: 10.1002/cpe.5756
Zicong Gao 1 , Weiyu Dong 1 , Rui Chang 2 , Yisen Wang 1
Affiliation  

Fuzzing is an effective approach to detect software vulnerabilities utilizing changeable generated inputs. However, fuzzing the network protocol on the firmware of IoT devices is limited by inefficiency of test case generation, cross-architecture instrumentation, and fault detection. In this article, we propose the Fw-fuzz, a coverage-guided and crossplatform framework for fuzzing network services running in the context of firmware on embedded architectures, which can generate more valuable test cases by introspecting program runtime information and using a genetic algorithm model. Specifically, we propose novel dynamic instrumentation in Fw-fuzz to collect the running state of the firmware program. Then Fw-fuzz adopts a genetic algorithm model to guide the generation of inputs with high code coverage. We fully implement the prototype system of Fw-fuzz and conduct evaluations on network service programs of various architectures in MIPS, ARM, and PPC. By comparing with the protocol fuzzers Boofuzz and Peach in metrics of edge coverage, our prototype system achieves an average growth of 33.7% and 38.4%, respectively. We further verify six known vulnerabilities and discover 5 0-day vulnerabilities with the Fw-fuzz, which prove the validity and utility of our framework. The overhead of our system expressed as an additional 5% of memory growth.

中文翻译:

Fw-fuzz:固件上网络协议的代码覆盖率指导的模糊测试框架

Fuzzing 是一种利用可变生成输入检测软件漏洞的有效方法。然而,在物联网设备的固件上模糊网络协议受到测试用例生成、跨架构仪器和故障检测效率低下的限制。在本文中,我们提出了 Fw-fuzz,这是一个覆盖引导和跨平台的框架,用于对在嵌入式架构上的固件上下文中运行的网络服务进行模糊测试,它可以通过内省程序运行时信息和使用遗传算法模型生成更有价值的测试用例。 . 具体来说,我们在 Fw-fuzz 中提出了新颖的动态检测来收集固件程序的运行状态。然后Fw-fuzz采用遗传算法模型来指导生成具有高代码覆盖率的输入。我们全面实现了 Fw-fuzz 的原型系统,并对 MIPS、ARM 和 PPC 中各种架构的网络服务程序进行了评估。通过与协议模糊器 Boofuzz 和 Peach 在边缘覆盖率指标上的比较,我们的原型系统分别实现了 33.7% 和 38.4% 的平均增长。我们进一步验证了六个已知漏洞并使用 Fw-fuzz 发现了 5 个 0-day 漏洞,这证明了我们框架的有效性和实用性。我们系统的开销表示为额外 5% 的内存增长。我们进一步验证了六个已知漏洞并使用 Fw-fuzz 发现了 5 个 0-day 漏洞,这证明了我们框架的有效性和实用性。我们系统的开销表示为额外 5% 的内存增长。我们进一步验证了六个已知漏洞并使用 Fw-fuzz 发现了 5 个 0-day 漏洞,这证明了我们框架的有效性和实用性。我们系统的开销表示为额外 5% 的内存增长。
更新日期:2020-04-08
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