Fault Injection Modeling Attacks on 65 nm Arbiter and RO Sum PUFs via Environmental Changes

Abstract

Physically Unclonable Functions (PUFs) are emerging as hardware security primitives. So-called strong PUFs provide a mechanism to authenticate chips which is inherently unique for every manufactured sample. To prevent cloning, modeling of the challenge-response pair (CRP) behavior should be infeasible. Machine learning (ML) algorithms are a well-known threat. Recently, repeatability imperfections of PUF responses have been identified as another threat. CMOS device noise renders a significant fraction of the CRPs unstable, hereby providing a side channel for modeling attacks. In previous work, 65 nm arbiter PUFs have been modeled as such with accuracies exceeding 97%. However, more PUF evaluations were required than for state-of-the-art ML approaches. In this work, we accelerate repeatability attacks by increasing the fraction of unstable CRPs. Response evaluation faults are triggered via environmental changes hereby. The attack speed, which is proportional to the fraction of unstable CRPs, increases with a factor 2.4 for both arbiter and ring oscillator (RO) sum PUFs. Data originates from a 65 nm silicon chip and hence not from simulations.