一句话：利用偏置温度不稳定性与时序传感器，从云 FPGA 恢复已移除的数据和 AES 密钥。
该论文研究云 FPGA 中的数据残留威胁：远程攻击者可恢复属于先前用户或专有设计镜像的已移除数据，作者称之为 FPGA pentimento。其成因是底层晶体管受偏置温度不稳定性（BTI）影响，使先前数据在器件上留下可测量的退化痕迹。攻击者使用时数转换器（TDC）测量这种时序退化，从而（1）从 AWS marketplace 上可获取的加密 FPGA 设计镜像中提取专有细节或密钥，以及（2）恢复云 FPGA 前用户的信息。作者在 AWS F1 上验证了这些威胁模型，并在其中一种模型下成功恢复 AES 密钥。该工作表明云 FPGA 的多租户隔离面临残留数据泄漏风险。
Remote attackers can recover "FPGA pentimento" - long-removed data belonging to a prior user or proprietary design image on a cloud FPGA. Just as a pentimento of a painting can be exposed by infrared imaging, FPGA pentimentos can be exposed by signal timing sensors. The data constituting an FPGA pentimento is imprinted on the device through bias temperature instability effects on the underlying transistors. Measuring this degradation using a time-to-digital converter allows 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. These threat models are validated on AWS F1, with successful AES key recovery under one model.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2303-17881,
title = {Pentimento: Data Remanence in Cloud FPGAs},
author = {Colin Drewes and Olivia Weng and Andres Meza and Alric Althoff and David Kohlbrenner and Ryan Kastner and Dustin Richmond},
booktitle = {ASPLOS},
year = {2024},
doi = {10.1145/3620665.3640355}
}