Efficient greybox fuzzing of applications in Linux-based IoT devices via enhanced user-mode emulation

Abstract

Greybox fuzzing has become one of the most effective vulnerability discovery techniques. However, greybox fuzzing techniques cannot be directly applied to applications in IoT devices. The main reason is that executing these applications highly relies on specific system environments and hardware. To execute the applications in Linux-based IoT devices, most existing fuzzing techniques use full-system emulation for the purpose of maximizing compatibility. However, compared with user-mode emulation, full-system emulation suffersfrom great overhead. Therefore, some previous works, such as Firm-AFL, propose to combine full-system emulation and user-mode emulation to speed up the fuzzing process. Despite the attempts of trying to shift the application towards user-mode emulation, no existing technique supports to execute these applications fully in the user-mode emulation. To address this issue, we propose EQUAFL, which can automatically set up the execution environment to execute embedded applications under user-mode emulation. EQUAFL first executes the application under full-system emulation and observe for the key points where the program may get stuck or even crash during user-mode emulation. With the observed information, EQUAFL can migrate the needed environment for user-mode emulation. Then, EQUAFL uses an enhanced user-mode emulation to replay system calls of network, and resource management behaviors to fulfill the needs of the embedded application during its execution. We evaluate EQUAFL on 70 network applications from different series of IoT devices. The result shows EQUAFL outperforms the state-of-the-arts in fuzzing efficiency (on average, 26 times faster than AFL-QEMU with full-system emulation, 14 times than Firm-AFL). We have also discovered ten vulnerabilities including six CVEs from the tested firmware images.