Fw‐fuzz: A code coverage‐guided fuzzing framework for network protocols on firmware

Abstract

Summary 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.