Side-channel attacks are a major threat to the security of cryptographic implementations, particularly for small devices that are under the physical control of the adversary. While several strategies for protecting against side-channel attacks exist, these often fail in practice due to unintended interactions between values deep within the CPU. To detect and protect from side-channel attacks, several automated tools have recently been proposed; one of their common limitations is that they only support first-order leakage.

We present the first side-channel attack on full-fledged smartphones that recovers the elliptic curve secret scalar from the electromagnetic signal that corresponds to a single scalar-by-point multiplication in current versions of Libgcrypt, OpenSSL, HACL* and curve25519-donna. To avoid leaking information via side channels, these implementations follow the recommendations of RFC 7748 and use a constant-time conditional swap operation. Our attack targets signal differences created by systematic changes in operand values during this conditional swap operation. We deploy the attack, using low-cost equipment (<$800), against two Android-based mobile phones and against a Linux-based IoT development board. We repeat the attack 100 times, each time with a different scalar, on each device. In all of the implementations considered in this work, our attack successfully recovers the full secret key within seconds. To mitigate the attack we suggest randomizing the exclusive-or mask in the conditional swap operation. We show that this countermeasure is effective in preventing this and similar attacks.

Electromagnetic fault injection (EMFI) is a powerful active attack, requiring minimal modifications of the device under attack while having excellent penetration capabilities. The number of possible parameter combinations when characterizing an attack is usually huge, rendering exhaustive search impossible. In this work we present a novel evolutionary algorithm for optimizing the parameters for EM fault injection, which out-performs previous search methods for EMFI. The cryptographic device under attack is treated as a black box, with only a few very general assumptions on its inner workings. We test our evolutionary algorithm by attacking SHA-3 where we are able to obtain 40 times more faulty measurements and 20 times more distinct fault measurements than the random search. When coupled with the algebraic fault attack, we get 25% more exploitable faults per individual measurement.