In today's era of the Internet of Things, we are surrounded by security- and safety-critical, network-connected devices. In parallel with the rise in attacks on such devices, we have also seen an increase in devices that are abandoned, reached the end of their support periods, or will not otherwise receive future security updates. While this issue exists for a wide array of devices, those that use monolithic firmware, where the code and data are opaquely intermixed, have traditionally been difficult to examine and protect. In this paper, we explore the challenges of retrofitting monolithic firmware images with new security measures. First, we outline the steps any analyst must take to retrofit firmware, and show that previous work is missing crucial aspects of the process, which are required for a practical solution. We then automate three of these aspects-locating attacker-controlled input, a safe retrofit injection location, and self-checks preventing modifications-through the use of novel automated program analysis techniques. We assemble these analyses into a system, Shimware, that can simplify and facilitate the process of creating a retrofitted firmware image, once the vulnerability is identified. To evaluate Shimware, we employ both a synthetic evaluation and actual retrofitting of three case study devices: a networked bench power supply, a Bluetooth-enabled cardiac implant monitor, and a high-end programmable logic controller (PLC). Not only could our system identify the correct sources of input, injection locations, and self-checks, but it injected payloads to correct serious safety and security-critical vulnerabilities in these devices.

Given the increasing ubiquity of online embedded devices, analyzing their firmware is important to security, privacy, and safety. The tight coupling between hardware and firmware and the diversity found in embedded systems makes it hard to perform dynamic analysis on firmware. However, firmware developers regularly develop code using abstractions, such as Hardware Abstraction Layers (HALs), to simplify their job. We leverage such abstractions as the basis for the re-hosting and analysis of firmware. By providing high-level replacements for HAL functions (a process termed High-Level Emulation - HLE), we decouple the hardware from the firmware. This approach works by first locating the library functions in a firmware sample, through binary analysis, and then providing generic implementations of these functions in a full-system emulator. We present these ideas in a prototype system, HALucinator, able to re-host firmware, and allow the virtual device to be used normally. First, we introduce extensions to existing library matching techniques that are needed to identify library functions in binary firmware, to reduce collisions, and for inferring additional function names. Next, we demonstrate the re-hosting process, through the use of simplified handlers and peripheral models, which make the process fast, flexible, and portable between firmware samples and chip vendors. Finally, we demonstrate the practicality of HLE for security analysis, by supplementing HALucinator with the American Fuzzy Lop fuzzer, to locate multiple previously-unknown vulnerabilities in firmware middleware libraries.

The size and complexity of software is increasing, and security flaws are becoming more numerous, sophisticated, and impactful. While the vulnerability identification process (especially in hard-to-analyze binary programs) has traditionally been driven by highly skilled human analysts, this approach does not scale, given the vast amount of deployed software. Recently, the vulnerability analysis process has started to shift toward automated approaches. The DARPA Cyber Grand Challenge has played a key role in transforming disconnected research ideas into fully autonomous cyber reasoning systems that analyze code to find vulnerabilities, generate exploits to prove the existence of these vulnerabilities, and patch the vulnerable software. In this article, we discuss our cyber reasoning system, Mechanical Phish, which we have open-sourced; the lessons we learned in participating in this ground-breaking competition; and our system's performance as a tool in assisting humans during the DEF CON Capture-the-Flag competition, which followed the DARPA Cyber Grand Challenge.

Static binary rewriting has many important applications in reverse engineering, such as patching, code reuse, and instrumentation. Binary reassembling is an efficient solution for static binary rewriting. While there has been a proposed solution to the reassembly of binaries, an evaluation on a realworld binary dataset shows that it suffers from some problems that lead to breaking binaries. Those problems include incorrect symbolization of immediates, failure in identifying symbolizable constants, lack of pointer safety checks, and other issues. Failure in addressing those problems makes the existing approach unsuitable for real-world binaries, especially those compiled with optimizations enabled. In this paper, we present a new systematic approach for binary reassembling. Our new approach is implemented in a tool called Ramblr. We evaluate Ramblr on 106 real-world programs on Linux x86 and x86-64, and 143 programs collected from the Cyber Grand Challenge Qualification Event. All programs are compiled to binaries with a set of different compilation flags in order to cover as many real-world scenarios as possible. Ramblr successfully reassembles most of the binaries, which is an improvement over the state-of-the-art approach. It should be noted that our reassembling procedure yields no execution overhead and no size expansion.