PERSEUS - Probabilistic Evaluation of Random Probing SEcurity Using Efficient Sampling

Cryptographic implementations are inherently vulnerable to side-channel attacks, which exploit physical leakages such as power consumption. Masking has become the most widely adopted countermeasure to mitigate these threats, as it randomizes intermediate values and makes the leakage less exploitable. Yet, a central challenge remains: how to rigorously assess the concrete security level of masked implementations. To tackle this issue, the random probing model has emerged as a powerful abstraction. It formalizes leakage as random probes in the circuit and, importantly, the security in the noisy leakage model, which closely reflects the behavior of real embedded devices, reduces to security in the random probing model. Hence, proving security in the random probing model provides sound guarantees of practical resistance against side-channel attacks. Yet, the current state of the art on random probing compilers and verifiers suffers from a clear limitation: scalable approaches yield prohibitively large and inefficient circuits, while tighter methods do not scale to practical circuit sizes. In this work, we bridge this gap by introducing a new methodology that directly estimates the random probing security of large circuits through Monte Carlo sampling, combined with a pruning strategy that drastically reduces the sampling space. We implement our approach in a new tool, PERSEUS, which supports both gate and wire leakage models. Our experiments demonstrate that PERSEUS can efficiently evaluate masked implementations of AES-128 with $n=8$ shares, achieving security levels beyond 32 bits, thereby significantly advancing the state of the art in practical verification of side-channel countermeasures.