Towards Automated Dynamic Analysis for Linux-based Embedded Firmware
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Presented at NDSS 2016 by
Commercial-off-the-shelf (COTS) network-enabled embedded devices are usually controlled by vendor firmware to perform integral functions in our daily lives. From home and small office networking equipment, such as wireless routers, over network attached storage, and surveillance cameras these devices are operated by proprietary firmware. For example, wireless home routers are often the first and only line of defense that separates a home user's personal computing and information devices from the Internet. Such a vital and privileged position in the user's network requires that these devices operate securely. Unfortunately, recent research and anecdotal evidence suggest that such security assumptions are not at all upheld by the devices deployed around the world. A first step to assess the security of such embedded device firmware is the accurate identification of vulnerabilities. However, the market offers a large variety of these embedded devices, which severely impacts the scalability of existing approaches in this area. In this paper, we present FIRMADYNE, the first automated dynamic analysis system that specifically targets Linux-based firmware on network-connected COTS devices in a scalable manner. We identify a series of challenges inherent to the dynamic analysis of COTS firmware, and discuss how our design decisions address them. At its core, FIRMADYNE relies on software-based full system emulation with an instrumented kernel to achieve the scalability necessary to analyze thousands of firmware binaries automatically. We evaluate FIRMADYNE on a real-world dataset of 23,035 firmware images across 42 device vendors gathered by our system. Using a sample of 74 exploits on the 9,486 firmware images that our system can successfully extract, we discover that 895 firmware images spanning at least 90 distinct products are vulnerable to one or more of the sampled exploit(s). This includes 14 previously-unknown vulnerabilities that were discovered with the aid of our framework, which affect 86 firmware images spanning at least 14 distinct products. Furthermore, our results show that 11 of our tested attacks affect firmware images from more than one vendor, suggesting that code-sharing and common upstream manufacturers (OEMs) are quite prevalent.