TY - GEN
T1 - Dynamic Frequency-Based Fingerprinting Attacks against Modern Sandbox Environments
AU - Dipta, Debopriya Roy
AU - Tiemann, Thore
AU - Gülmezoğlu, Berk
AU - Marin, Eduard
AU - Eisenbarth, Thomas
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2024
Y1 - 2024
N2 - The cloud computing landscape has evolved sig-nificantly in recent years, embracing various sandboxes to meet the diverse demands of modern cloud applications. These sandboxes encompass container-based technologies like Docker and gVisor, microVM-based solutions like Fire-cracker, and security-centric sandboxes relying on Trusted Execution Environments (TEEs) such as Intel SGX and AMD SEV. However, the practice of placing multiple tenants on shared physical hardware raises security and privacy concerns, most notably side-channel attacks. In this paper, we investigate the possibility of fingerprinting containers through CPU frequency reporting sensors in Intel and AMD CPUs. One key enabler of our attack is that the current CPU frequency information can be accessed by user-space attackers. We demonstrate that Docker images exhibit a unique frequency signature, enabling the distinction of different containers with up to 84.5 % accuracy even when multiple containers are running simultaneously in different cores. Additionally, we assess the effectiveness of our attack when performed against several sandboxes deployed in cloud environments, including Google's gVisor, AWS' Firecracker, and TEE-based platforms like Gramine (utilizing Intel SGX) and AMD SEV. Our empirical results show that these attacks can also be carried out successfully against all of these sandboxes in less than 40 seconds, with an accuracy of over 70 % in all cases. Finally, we propose a noise injection-based countermeasure to mitigate the proposed attack on cloud environments.
AB - The cloud computing landscape has evolved sig-nificantly in recent years, embracing various sandboxes to meet the diverse demands of modern cloud applications. These sandboxes encompass container-based technologies like Docker and gVisor, microVM-based solutions like Fire-cracker, and security-centric sandboxes relying on Trusted Execution Environments (TEEs) such as Intel SGX and AMD SEV. However, the practice of placing multiple tenants on shared physical hardware raises security and privacy concerns, most notably side-channel attacks. In this paper, we investigate the possibility of fingerprinting containers through CPU frequency reporting sensors in Intel and AMD CPUs. One key enabler of our attack is that the current CPU frequency information can be accessed by user-space attackers. We demonstrate that Docker images exhibit a unique frequency signature, enabling the distinction of different containers with up to 84.5 % accuracy even when multiple containers are running simultaneously in different cores. Additionally, we assess the effectiveness of our attack when performed against several sandboxes deployed in cloud environments, including Google's gVisor, AWS' Firecracker, and TEE-based platforms like Gramine (utilizing Intel SGX) and AMD SEV. Our empirical results show that these attacks can also be carried out successfully against all of these sandboxes in less than 40 seconds, with an accuracy of over 70 % in all cases. Finally, we propose a noise injection-based countermeasure to mitigate the proposed attack on cloud environments.
UR - https://www.mendeley.com/catalogue/e4ba39a1-44bc-3d59-8406-532dbf0a1f04/
U2 - 10.1109/EuroSP60621.2024.00025
DO - 10.1109/EuroSP60621.2024.00025
M3 - Conference contribution
SN - 9798350354256
T3 - Proceedings - 9th IEEE European Symposium on Security and Privacy, Euro S and P 2024
SP - 327
EP - 344
BT - 9th IEEE European Symposium on Security and Privacy (EuroS&P 2024), July 8-12, 2024, Vienna, Austria
ER -