Software Grand Exposure: SGX Cache Attacks Are Practical
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Abstract (arXiv)
Side-channel information leakage is a known limitation of SGX. Researchers have demonstrated that secret-dependent information can be extracted from enclave execution through page-fault access patterns. Consequently, various recent research efforts are actively seeking countermeasures to SGX side-channel attacks. It is widely assumed that SGX may be vulnerable to other side channels, such as cache access pattern monitoring, as well. However, prior to our work, the practicality and the extent of such information leakage was not studied. In this paper we demonstrate that cache-based attacks are indeed a serious threat to the confidentiality of SGX-protected programs. Our goal was to design an attack that is hard to mitigate using known defenses, and therefore we mount our attack without interrupting enclave execution. This approach has major technical challenges, since the existing cache monitoring techniques experience significant noise if the victim process is not interrupted. We designed and implemented novel attack techniques to reduce this noise by leveraging the capabilities of the privileged adversary. Our attacks are able to recover confidential information from SGX enclaves, which we illustrate in two example cases: extraction of an entire RSA-2048 key during RSA decryption, and detection of specific human genome sequences during genomic indexing. We show that our attacks are more effective than previous cache attacks and harder to mitigate than previous SGX side-channel attacks.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Feb 2017.
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Citing papers per year: 2021: 4; 2022: 4; 2023: 0; 2024: 1; 2025: 2; 2026: 3.
14 papers.
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- 2022CrowdGuard: Federated Backdoor Detection in Federated Learningcited by 26
- 2022DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardwarecited by 13
- 2021secureTF: A Secure TensorFlow Frameworkcited by 12
- 2021MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layerscited by 5
- 2021Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspectivecited by 4
- 2022OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsificationcited by 4
- 2022Edge Security: Challenges and Issuescited by 3
- 2025Activation Functions Considered Harmful: Recovering Neural Network Weights through Controlled Channelscited by 2
- 2021Automated Side Channel Analysis of Media Software with Manifold Learningcited by 1
- 2025How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computationscited by 1
- 2024How To Think About End-To-End Encryption and AI: Training, Processing, Disclosure, and Consentcited by 0
- 2026Health+: Empowering Individuals via Unifying Health Datacited by 0
2 more
- 2026Downsides of Smartness Across Edge-Cloud Continuum in Modern Industry31 Mar 2026
- 2026RobPI: Robust Private Inference against Malicious Client23 Feb 2026
- 2026Health+: Empowering Individuals via Unifying Health Data22 Feb 2026
- 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
- 2024How To Think About End-To-End Encryption and AI: Training, Processing, Disclosure, and Consent28 Dec 2024
- 2022CrowdGuard: Federated Backdoor Detection in Federated Learning14 Oct 2022
- 2022DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware30 Jun 2022
- 2022Edge Security: Challenges and Issues14 Jun 2022
- 2022OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification15 Feb 2022
- 2021Automated Side Channel Analysis of Media Software with Manifold Learning9 Dec 2021
- 2021Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective5 Nov 2021
2 more
- 2022CrowdGuard: Federated Backdoor Detection in Federated Learningcited by 26
- 2022DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardwarecited by 13
- 2021secureTF: A Secure TensorFlow Frameworkcited by 12
- 2021MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layerscited by 5
- 2021Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspectivecited by 4
- 2022OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsificationcited by 4
- 2022Edge Security: Challenges and Issuescited by 3
- 2025Activation Functions Considered Harmful: Recovering Neural Network Weights through Controlled Channelscited by 2
- 2021Automated Side Channel Analysis of Media Software with Manifold Learningcited by 1
- 2025How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computationscited by 1
- 2024How To Think About End-To-End Encryption and AI: Training, Processing, Disclosure, and Consentcited by 0
- 2026Health+: Empowering Individuals via Unifying Health Datacited by 0
12 most cited of 14 All 14 (JSON)
- 2026Downsides of Smartness Across Edge-Cloud Continuum in Modern Industry31 Mar 2026
- 2026RobPI: Robust Private Inference against Malicious Client23 Feb 2026
- 2026Health+: Empowering Individuals via Unifying Health Data22 Feb 2026
- 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
- 2024How To Think About End-To-End Encryption and AI: Training, Processing, Disclosure, and Consent28 Dec 2024
- 2022CrowdGuard: Federated Backdoor Detection in Federated Learning14 Oct 2022
- 2022DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware30 Jun 2022
- 2022Edge Security: Challenges and Issues14 Jun 2022
- 2022OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification15 Feb 2022
- 2021Automated Side Channel Analysis of Media Software with Manifold Learning9 Dec 2021
- 2021Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective5 Nov 2021
12 newest of 14 All 14 (JSON)