Leaky Cauldron on the Dark Land: Understanding Memory Side-Channel Hazards in SGX
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Abstract (arXiv)
Side-channel risks of Intel's SGX have recently attracted great attention. Under the spotlight is the newly discovered page-fault attack, in which an OS-level adversary induces page faults to observe the page-level access patterns of a protected process running in an SGX enclave. With almost all proposed defense focusing on this attack, little is known about whether such efforts indeed raise the bar for the adversary, whether a simple variation of the attack renders all protection ineffective, not to mention an in-depth understanding of other attack surfaces in the SGX system. In the paper, we report the first step toward systematic analyses of side-channel threats that SGX faces, focusing on the risks associated with its memory management. Our research identifies 8 potential attack vectors, ranging from TLB to DRAM modules. More importantly, we highlight the common misunderstandings about SGX memory side channels, demonstrating that high frequent AEXs can be avoided when recovering EdDSA secret key through a new page channel and fine-grained monitoring of enclave programs (at the level of 64B) can be done through combining both cache and cross-enclave DRAM channels. Our findings reveal the gap between the ongoing security research on SGX and its side-channel weaknesses, redefine the side-channel threat model for secure enclaves, and can provoke a discussion on when to use such a system and how to use it securely.
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 May 2017.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 2; 2024: 2; 2025: 4; 2026: 2.
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- 2022Verifiable and Provably Secure Machine Unlearningcited by 24
- 2022DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardwarecited by 13
- 2021secureTF: A Secure TensorFlow Frameworkcited by 12
- 2021SoK: Privacy-Preserving Collaborative Tree-based Model Learningcited by 4
- 2024TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Modelscited by 3
- 2023Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inferencecited by 2
- 2025Activation Functions Considered Harmful: Recovering Neural Network Weights through Controlled Channelscited by 2
- 2022Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systemscited by 1
- 2024Dynamic Frequency-Based Fingerprinting Attacks against Modern Sandbox Environmentscited by 1
- 2025How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computationscited by 1
- 2025CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computationcited by 1
- 2023Integrating Homomorphic Encryption and Trusted Execution Technology for Autonomous and Confidential Model Refining in Cloudcited by 0
3 more
- 2026TrEEStealer: Stealing Decision Trees via Enclave Side Channels20 Apr 2026
- 2026Privacy-Preserving Iris Recognition: Performance Challenges and Outlook27 Mar 2026
- 2025CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation24 Nov 2025
- 2025Reliable and Private Utility Signaling for Data Markets11 Nov 2025
- 2025How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations2 Jul 2025
- 2025Activation Functions Considered Harmful: Recovering Neural Network Weights through Controlled Channels24 Mar 2025
- 2024TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models15 Nov 2024
- 2024Dynamic Frequency-Based Fingerprinting Attacks against Modern Sandbox Environments16 Apr 2024
- 2023Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inference9 Sep 2023
- 2023Integrating Homomorphic Encryption and Trusted Execution Technology for Autonomous and Confidential Model Refining in Cloud2 Aug 2023
- 2022Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems12 Dec 2022
- 2022Verifiable and Provably Secure Machine Unlearning17 Oct 2022
- 2022Verifiable and Provably Secure Machine Unlearningcited by 24
- 2022Verifiable and Provably Secure Machine Unlearning17 Oct 2022
- 2022DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardwarecited by 13
- 2021secureTF: A Secure TensorFlow Frameworkcited by 12
- 2021SoK: Privacy-Preserving Collaborative Tree-based Model Learningcited by 4
- 2024TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Modelscited by 3
- 2023Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inferencecited by 2
- 2025Activation Functions Considered Harmful: Recovering Neural Network Weights through Controlled Channelscited by 2
- 2022Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systemscited by 1
- 2024Dynamic Frequency-Based Fingerprinting Attacks against Modern Sandbox Environmentscited by 1
- 2025How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computationscited by 1
- 2025CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computationcited by 1
- 2023Integrating Homomorphic Encryption and Trusted Execution Technology for Autonomous and Confidential Model Refining in Cloudcited by 0
- 2025Reliable and Private Utility Signaling for Data Marketscited by 0
12 most cited of 14 All 14 (JSON)
- 2026TrEEStealer: Stealing Decision Trees via Enclave Side Channels20 Apr 2026
- 2026Privacy-Preserving Iris Recognition: Performance Challenges and Outlook27 Mar 2026
- 2025CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation24 Nov 2025
- 2025Reliable and Private Utility Signaling for Data Markets11 Nov 2025
- 2025How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations2 Jul 2025
- 2025Activation Functions Considered Harmful: Recovering Neural Network Weights through Controlled Channels24 Mar 2025
- 2024TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models15 Nov 2024
- 2024Dynamic Frequency-Based Fingerprinting Attacks against Modern Sandbox Environments16 Apr 2024
- 2023Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inference9 Sep 2023
- 2023Integrating Homomorphic Encryption and Trusted Execution Technology for Autonomous and Confidential Model Refining in Cloud2 Aug 2023
- 2022Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems12 Dec 2022
- 2022DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware30 Jun 2022
12 newest of 14 All 14 (JSON)