Avatar: Reinforcing Fault Attack Countermeasures in EDA with Fault Transformations

Cryptography hardware are highly vulnerable to a class of side-channel attacks known as Differential Fault Analysis (DFA). These attacks exploit fault induced errors to compromise secret keys from ciphers within a few seconds. A bias in the error probabilities strengthens the attack considerably. It abets in bypassing countermeasures and is also the basis of powerful attack variants like the Differential Fault Intensity Analysis (DFIA) and Statistical Ineffective Fault Analysis (SIFA). In this paper, we make two significant contributions. First, we identify the correlation between fault induced errors and gatelevel parameters like the threshold voltage, gate size, and
${V_{\text{DD}}}$
. We show how these parameters can influence the bias in the error probabilities. Then, we propose an algorithm, called Avatar, that carefully tunes gate-level parameters to strengthen the redundancy countermeasures against DFA, DFIA, and SIFA attacks with no additional logic needed. The central idea of Avatar is to reconfigure gates in the redundant circuits so that each circuit has a unique behavior to faults, making fault detection much more efficient. In AES for instance, fault attack resistance improves by 40% for DFA and DFIA, and 99% in the case of SIFA. Avatar incurs negligible area overheads and can be quickly adopted in any cipher design. It can be incorporated in commercial EDA flows and provides users with tunable knobs to trade-off performance and power consumption, for fault attack security.