International Association
for Cryptologic Research

Hardware faults are a known source of security vulnerabilities. Fault injection in secure embedded systems leads to information leakage and privilege escalation, and countless fault attacks have been demonstrated both in simulation and in practice. However, there is a significant gap between simulated fault attacks and physical fault attacks. Simulations use idealized fault models such as single-bit flips with uniform distribution. These ideal fault models may not hold in practice. On the other hand, practical experiments lack the white-box visibility necessary to determine the true nature of the fault, leading to probabilistic vulnerability assessments and unexplained results. In embedded software, this problem is further exacerbated by the layered abstractions between the hardware (where the fault originates) and the application software (where the fault effect is observed). We present FaultDetective, a method to investigate the root-cause of fault injection from fault detection in software. Our main insight is that fault detection in software is only the end-point of a chain of events that starts with a fault manifestation in hardware and propagates through the micro-architecture and architecture before reaching the software level. To understand the fault effects at the hardware level, we use a scan chain, a low-level hardware test structure. We then use white-box simulation to propagate and observe hardware faults in the embedded software. We efficiently visualize the fault propagation across abstraction levels using a hash-tree representation of the scan chain. We implement this concept in a multi-core MSP430 micro-controller that redundantly executes an application in lock-step. With this setup, we observe the fault effects for several different stressors, including clock glitching and thermal laser stimulation, and explain the root-cause in each case.

Active fault injection is a credible threat to real-world digital systems computing on sensitive data. Arguing about security in the presence of faults is non-trivial, and state-of-the-art criteria are overly conservative and lack the ability of fine-grained comparison. However, comparing two alternative implementations for their security is required to find a satisfying compromise between security and performance. In addition, the comparison of alternative fault scenarios can help optimize the implementation of effective countermeasures. In this work, we use quantitative information flow analysis to establish a vulnerability metric for hardware circuits under fault injection that measures the severity of an attack in terms of information leakage. Potential use cases range from comparing implementations with respect to their vulnerability to specific fault scenarios to optimizing countermeasures. We automate the computation of our metric by integrating it into a state-of-the-art evaluation tool for physical attacks and provide new insights into the security under an active fault attacker.

Finding the root cause of power-based side-channel leakage becomes harder when multiple layers of design abstraction are involved. While side-channel leakage originates in processor hardware, the dangerous consequences may only become apparent in the cryptographic software that runs on the processor. This contribution presents RootCanal, a methodology to explain the origin of side-channel leakage in a software program in terms of the underlying micro-architecture and system architecture. We simulate the hardware power consumption at the gate level and perform a non-specific test to identify the logic gates that contribute most sidechannel leakage. Then, we back-annotate those findings to the related activities in the software. The resulting analysis can automatically point out non-trivial causes of side-channel leakages. To illustrate RootCanal’s capabilities, we discuss a collection of case studies.

Physical attacks can compromise the security of cryptographic devices. Depending on the attack’s requirements, adversaries might need to (i) place probes in the proximity of the integrated circuits (ICs) package, (ii) create physical connections between their probes/wires and the system’s PCB, or (iii) physically tamper with the PCB’s components, chip’s package, or substitute the entire PCB to prepare the device for the attack. While tamper-proof enclosures prevent and detect physical access to the system, their high manufacturing cost and incompatibility with legacy systems make them unattractive for many low-cost scenarios. In this paper, inspired by methods known from the field of power integrity analysis, we demonstrate how the impedance characterization of the system’s power distribution network (PDN) using on-chip circuit-based network analyzers can detect various classes of tamper events. We explain how these embedded network analyzers, without any modifications to the system, can be deployed on FPGAs to extract the frequency response of the PDN. The analysis of these frequency responses reveals different classes of tamper events from board to chip level. To validate our claims, we run an embedded network analyzer on FPGAs of a family of commercial development kits and perform extensive measurements for various classes of PCB and IC package tampering required for conducting different side-channel or fault attacks. Using the Wasserstein Distance as a statistical metric, we further show that we can confidently detect tamper events. Our results, interestingly, show that even environment-level tampering activities, such as the proximity of contactless EM probes to the IC package or slightly polished IC package, can be detected using on-chip impedance sensing.