Malware Guard Extension: Using SGX to Conceal Cache Attacks
Not on the map. Why?
Abstract (arXiv)
In modern computer systems, user processes are isolated from each other by the operating system and the hardware. Additionally, in a cloud scenario it is crucial that the hypervisor isolates tenants from other tenants that are co-located on the same physical machine. However, the hypervisor does not protect tenants against the cloud provider and thus the supplied operating system and hardware. Intel SGX provides a mechanism that addresses this scenario. It aims at protecting user-level software from attacks from other processes, the operating system, and even physical attackers. In this paper, we demonstrate fine-grained software-based side-channel attacks from a malicious SGX enclave targeting co-located enclaves. Our attack is the first malware running on real SGX hardware, abusing SGX protection features to conceal itself. Furthermore, we demonstrate our attack both in a native environment and across multiple Docker containers. We perform a Prime+Probe cache side-channel attack on a co-located SGX enclave running an up-to-date RSA implementation that uses a constant-time multiplication primitive. The attack works although in SGX enclaves there are no timers, no large pages, no physical addresses, and no shared memory. In a semi-synchronous attack, we extract 96% of an RSA private key from a single trace. We extract the full RSA private key in an automated attack from 11 traces within 5 minutes.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Feb 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 3; 2023: 0; 2024: 0; 2025: 1.
7 papers.
- 2022Fine-grained Poisoning Attack to Local Differential Privacy Protocols for Mean and Variance Estimationcited by 9
- 2022Network Shuffling: Privacy Amplification via Random Walkscited by 6
- 2021Ownership Verification of DNN Architectures via Hardware Cache Side Channelscited by 3
- 2021Plinius: Secure and Persistent Machine Learning Model Trainingcited by 3
- 2021Automated Side Channel Analysis of Media Software with Manifold Learningcited by 1
- 2025Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocolscited by 1
- 2022MAD-EN: Microarchitectural Attack Detection through System-wide Energy Consumptioncited by 0
- 2025Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols11 Jun 2025
- 2022MAD-EN: Microarchitectural Attack Detection through System-wide Energy Consumption31 May 2022
- 2022Fine-grained Poisoning Attack to Local Differential Privacy Protocols for Mean and Variance Estimation24 May 2022
- 2022Network Shuffling: Privacy Amplification via Random Walks8 Apr 2022
- 2021Automated Side Channel Analysis of Media Software with Manifold Learning9 Dec 2021
- 2021Plinius: Secure and Persistent Machine Learning Model Training7 Apr 2021
- 2021Ownership Verification of DNN Architectures via Hardware Cache Side Channels6 Feb 2021