With the increasing complexity of digital applications, the use of variable-length instruction sets became essential, in order to achieve higher code density and thus better performance. However, security aspects must always be considered, in particular with the significant improvement of attack techniques and equipment. Fault injection, in particular, is among the most interesting and promising attack techniques thanks to the recent advancements. In this article, we provide proper characterization, at the instruction set architecture (ISA) level, for several faulty behaviors that can be obtained when targeting a variable-length instruction set. We take into account the binary encoding of instructions, and show how the obtained behaviors depend on the alignment of the instructions in the memory. Moreover, we are also able to give a better insight on previous results from the literature, that were still partially unexplained. We also show how the observed behaviors can be exploited in various security contexts.
Fault injection attacks are considered one of the major threats to cyber-physical systems. The increasing complexity of embedded microprocessors involves complicated behaviours in presence of such attacks. Realistic fault models are required to study code vulnerabilities and be able to protect digital systems from these attacks. However, inferring fault models using only limited observations of faulty microprocessors is difficult. In this article, we present experiments that show the difficulty of characterizing and modelling the fault injection effects. From there, we propose a complete approach for fault analysis to build proper fault models at different system levels, which will help in designing suitable countermeasures at reasonable cost.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.