Hovav ShachamI guarantee that this is the wildest paper you’ll read all year. Drewes et al., “Pentimento: Data Remanence in Cloud FPGAs,” https://arxiv.org/abs/2303.17881
We find that a remote attacker can recover “FPGA pentimentos” — long-removed secret data belonging to a prior user or proprietary design image on a cloud FPGA. Just as a pentimento of a painting can be exposed via infrared imaging, FPGA pentimentos can be exposed via signal
timing sensors instantiated on a remote cloud FPGA. The sensitive data constituting an FPGA pentimento is imprinted to the device through bias temperature instability effects on the underlying transistors. We demonstrate how this slight degradation can be measured using a time-to-digital converter when an adversary programs one into the target cloud FPGA. This technique allows an attacker to ascertain previously safe information, after it is no longer explicitly present, on cloud FPGAs. Notably, it can allow an attacker to (1) extract proprietary details or keys from an encrypted FPGA design image available on the AWS marketplace and (2) recover information from a previous user of a cloud-FPGA. Both threat models are experimentally validated on the AWS F1 platform.
Pentimento: Data Remanence in Cloud FPGAs
Cloud FPGAs strike an alluring balance between computational efficiency, energy efficiency, and cost. It is the flexibility of the FPGA architecture that enables these benefits, but that very same flexibility that exposes new security vulnerabilities. We show that a remote attacker can recover "FPGA pentimenti" - long-removed secret data belonging to a prior user of a cloud FPGA. The sensitive data constituting an FPGA pentimento is an analog imprint from bias temperature instability (BTI) effects on the underlying transistors. We demonstrate how this slight degradation can be measured using a time-to-digital (TDC) converter when an adversary programs one into the target cloud FPGA. This technique allows an attacker to ascertain previously safe information on cloud FPGAs, even after it is no longer explicitly present. Notably, it can allow an attacker who knows a non-secret "skeleton" (the physical structure, but not the contents) of the victim's design to (1) extract proprietary details from an encrypted FPGA design image available on the AWS marketplace and (2) recover data loaded at runtime by a previous user of a cloud FPGA using a known design. Our experiments show that BTI degradation (burn-in) and recovery are measurable and constitute a security threat to commercial cloud FPGAs.
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