A Survey of Microarchitectural Side-channel Vulnerabilities, Attacks and Defenses in Cryptography
Not on the map. Why?
Abstract (arXiv)
Side-channel attacks have become a severe threat to the confidentiality of computer applications and systems. One popular type of such attacks is the microarchitectural attack, where the adversary exploits the hardware features to break the protection enforced by the operating system and steal the secrets from the program. In this paper, we systematize microarchitectural side channels with a focus on attacks and defenses in cryptographic applications. We make three contributions. (1) We survey past research literature to categorize microarchitectural side-channel attacks. Since these are hardware attacks targeting software, we summarize the vulnerable implementations in software, as well as flawed designs in hardware. (2) We identify common strategies to mitigate microarchitectural attacks, from the application, OS and hardware levels. (3) We conduct a large-scale evaluation on popular cryptographic applications in the real world, and analyze the severity, practicality and impact of side-channel vulnerabilities. This survey is expected to inspire side-channel research community to discover new attacks, and more importantly, propose new defense solutions against them.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Mar 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 3.
5 papers.
- 2023Mercury: An Automated Remote Side-channel Attack to Nvidia Deep Learning Acceleratorcited by 4
- 2025Securing Transformer-based AI Execution via Unified TEEs and Crypto-protected Acceleratorscited by 2
- 2025Onboard Optimization and Learning: A Surveycited by 1
- 2025SVAgent: AI Agent for Hardware Security Verification Assertioncited by 1
- 2024Memory-Efficient and Secure DNN Inference on TrustZone-enabled Consumer IoT Devicescited by 0
- 2025SVAgent: AI Agent for Hardware Security Verification Assertion22 Jul 2025
- 2025Securing Transformer-based AI Execution via Unified TEEs and Crypto-protected Accelerators4 Jul 2025
- 2025Onboard Optimization and Learning: A Survey7 May 2025
- 2024Memory-Efficient and Secure DNN Inference on TrustZone-enabled Consumer IoT Devices19 Mar 2024
- 2023Mercury: An Automated Remote Side-channel Attack to Nvidia Deep Learning Accelerator2 Aug 2023