Fuzzing embedded systems is hard. Their key components – microcontrollers – are highly diverse and cannot be easily virtualized; their software may not be changed or instrumented. However, we observe that many, if not most, microcontrollers feature a debug interface through which a debug probe (typically controllable via GDB, the GNU debugger) can set a limited number of hardware breakpoints. Using these, we extract partial coverage feedback even for uninstrumented binary code; and thus enable effective fuzzing for embedded systems through a generic, widespread mechanism. In its evaluation on four different microcontroller boards, our prototypical implementation GDBFuzz quickly reaches high code coverage and detects known and new vulnerabilities. As it can be applied to any program and system that GDB can debug, GDBFuzz is one of the least demanding and most versatile coverage-guided fuzzers.

Abstract Fuzzing has become one of the best-established methods to uncover software bugs. Meanwhile, the market of embedded systems, which binds the software execution tightly to the very hardware architecture, has grown at a steady pace, and that pace is anticipated to become yet more sustained in the near future. Embedded systems also benefit from fuzzing, but the innumerable existing architectures and hardware peripherals complicate the development of general and usable approaches, hence a plethora of tools have recently appeared. Here comes a stringent need for a systematic review in the area of fuzzing approaches for embedded systems, which we term “embedded fuzzing” for brevity. The inclusion criteria chosen in this article are semi-objective in their coverage of the most relevant publication venues as well as of our personal judgement. The review rests on a formal definition we develop to represent the realm of embedded fuzzing. It continues by discussing the approaches that satisfy the inclusion criteria, then defines the relevant elements of comparison and groups the approaches according to how the execution environment is served to the system under test. The resulting review produces a table with 42 entries, which in turn supports discussion suggesting vast room for future research due to the limitations noted.

Increased connectivity increases the attack vector. This also applies to connected vehicles in which vulnerabilities not only threaten digital values but also humans and the environment. Typically, attackers try to exploit the Controller Area Network (CAN) bus, which is the most widely used standard for internal vehicle communication. Once an Electronic Control Unit (ECU) connected to the CAN bus is compromised, attackers can manipulate messages at will. The missing sender authentication by design of the CAN bus enables adversarial access to vehicle functions with severe consequences. In order to address this problem, we propose Scission, an Intrusion Detection System (IDS) which uses fingerprints extracted from CAN frames, enabling the identification of sending ECUs. Scission utilizes physical characteristics from analog values of CAN frames to assess whether it was sent by the legitimate ECU. In addition, to detect comprised ECUs, the proposed system is able to recognize attacks from unmonitored and additional devices. We show that Scission is able to identify the sender with an average probability of 99.85%, during the evaluation on two series production cars and a prototype setup. Due to the robust design of the system, the evaluation shows that all false positives were prevented. Compared to previous approaches, we have significantly reduced hardware costs and increased identification rates, which enables a broad application of this technology.

A key requirement for most security solutions is to provide secure cryptographic key storage in a way that will easily scale in the age of the Internet of Things. In this paper, we focus on providing such a solution based on Physical Unclonable Functions (PUFs). To this end, we focus on microelectromechanical systems (MEMS)-based gyroscopes and show via wafer-level measurements and simulations, that it is feasible to use the physical and electrical properties of these sensors for cryptographic key generation. After identifying the most promising features, we propose a novel quantization scheme to extract bit strings from the MEMS analog measurements. We provide upper and lower bounds for the minimum entropy of the derived bit strings and fully analyze the intra- and inter-class distributions across the operation range of the MEMS device. We complement these measurements via Monte-Carlo simulations based on the distributions of the parameters measured on actual devices. We also propose and evaluate a complete cryptographic key generation chain based on fuzzy extractors. We derive a full entropy 128-bit key using the obtained min-entropy estimates, requiring 1219 bits of helper data with an (authentication) failure probability of 4 . 10-7. In addition, we propose a dedicated MEMS-PUF design, which is superior to our measured sensor, in terms of chip area, quality and quantity of key seed features.