Muhui Jiang

Dingding Wang, Muhui Jiang, Rui Chang et al.

Embedded devices are becoming popular. Meanwhile, researchers are actively working on improving the security of embedded devices. However, previous work ignores the insecurity caused by a special category of devices, i.e., the End-of-Life (EoL in short) devices. Once a product becomes End-of-Life, vendors tend to no longer maintain its firmware or software, including providing bug fixes and security patches. This makes EoL devices susceptible to attacks. For instance, a report showed that an EoL model with thousands of active devices was exploited to redirect web traffic for malicious purposes. In this paper, we conduct the first measurement study to shed light on the (in)security of EoL devices. To this end, our study performs two types of analysis, including the aliveness analysis and the vulnerability analysis. The first one aims to detect the scale of EoL devices that are still alive. The second one is to evaluate the vulnerabilities existing in (active) EoL devices. We have applied our approach to a large number of EoL models from three vendors (i.e., D-Link, Tp-Link, and Netgear) and detect the alive devices in a time period of ten months. Our study reveals some worrisome facts that were unknown by the community. For instance, there exist more than 2 million active EoL devices. Nearly 300,000 of them are still alive even after five years since they became EoL. Although vendors may release security patches after the EoL date, however, the process is ad hoc and incomplete. As a result, more than 1 million active EoL devices are vulnerable, and nearly half of them are threatened by high-risk vulnerabilities. Attackers can achieve a minimum of 2.79 Tbps DDoS attack by compromising a large number of active EoL devices. We believe these facts pose a clear call for more attention to deal with the security issues of EoL devices.

Muhui Jiang, Lin Ma, Yajin Zhou et al.

Dynamic analysis based on the full-system emulator QEMU is widely used for various purposes. However, it is challenging to run firmware images of embedded devices in QEMU, especially the process to boot the Linux kernel (we call this process rehosting the Linux kernel.) That's because embedded devices usually use different system-on-chips (SoCs) from multiple vendors and only a limited number of SoCs are currently supported in QEMU. In this work, we propose a technique called peripheral transplantation. The main idea is to transplant the device drivers of designated peripherals into the Linux kernel. By doing so, it can replace the peripherals in the kernel that are currently unsupported in QEMU with supported ones, thus making the Linux kernel rehostable. After that, various applications can be built upon. We implemented this technique inside a prototype system called ECMO and applied it to 815 firmware images, which consist of 20 kernel versions, 37 device models, and 24 vendors. The result shows that ECMO can successfully transplant peripherals for all the 815 Linux kernels. Among them,710 kernels can be successfully rehosted, i.e., launching a user-space shell (87.1% success rate). The failed cases are mainly because the root file system format (ramfs) is not supported by the kernel. We further build three applications, i.e., kernel crash analysis, rootkit forensic analysis, and kernel fuzzing, based on the rehosted kernels to demonstrate the usage scenarios of ECMO

Muhui Jiang, Tianyi Xu, Yajin Zhou et al.

Emulator is widely used to build dynamic analysis frameworks due to its fine-grained tracing capability, full system monitoring functionality, and scalability of running on different operating systemsand architectures. However, whether the emulator is consistent with real devices is unknown. To understand this problem, we aim to automatically locate inconsistent instructions, which behave differently between emulators and real devices. We target ARM architecture, which provides machine readable specification. Based on the specification, we propose a test case generator by designing and implementing the first symbolic execution engine for ARM architecture specification language (ASL). We generate 2,774,649 representative instruction streams and conduct differential testing with these instruction streams between four ARM real devices in different architecture versions (i.e., ARMv5, ARMv6, ARMv7-a, and ARMv8-a) and the state-of-the-art emulators (i.e., QEMU). We locate 155,642 inconsistent instruction streams, which cover 30% of all instruction encodings and 47.8% of the instructions. We find undefined implementation in ARM manual and implementation bugs of QEMU are the major causes of inconsistencies. Furthermore, we discover four QEMU bugs, which are confirmed and patched by thedevelopers, covering 13 instruction encodings including the most commonly used ones (e.g.,STR,BLX). With the inconsistent instructions, we build three security applications and demonstrate thecapability of these instructions on detecting emulators, anti-emulation, and anti-fuzzing.