Hardware and software of secured embedded systems are prone to physical attacks. In particular, fault injection attacks revealed vulnerabilities on the data and the control flow allowing an attacker to break cryptographic or secured algorithms implementations. While many research studies concentrated on successful attacks on the data flow, only a few targets the instruction flow. In this paper, we focus on electromagnetic fault injection (EMFI) on the control flow, especially on the instruction cache. We target the very widespread (smartphones, tablets, settop-boxes, health-industry monitors and sensors, etc.) ARMv7-M architecture. We describe a practical EMFI platform and present a methodology providing high control level and high reproducibility over fault injections. Indeed, we observe that a precise fault model occurs in up to 96% of the cases. We then characterize and exhibit this practical fault model on the cache that is not yet considered in the literature. We comprehensively describe its effects and show how it can be used to reproduce well known fault attacks. Finally, we describe how it can benefits attackers to mount new powerful attacks or simplify existing ones.

International audiencePhysical fault injections break security functionalities of algorithms by targeting their implementations. Software techniques strengthen such implementations to enhance their robustness against fault attacks. Exhaustively testing physical fault injections is time consuming and requires complex platforms. Simulation solutions are developed for this specific purpose. We chose two independent tools presented in 2014, the Laser Attack Robustness (Lazart) and the Embedded Fault Simulator (EFS) in order to evaluate software implementations against multiple fault injection attacks. Lazart and the EFS share the common goal that consists in detecting vulnerabilities in the code. However, they operate with different techniques , fault models and abstraction levels. This paper aims at exhibiting specific advantages of both approaches and proposes a combining scheme that emphasizes their complementary nature