# Software Grand Exposure: SGX Cache Attacks Are Practical

arXiv: https://arxiv.org/abs/1702.07521 · HTML: https://mlsanity.com/paper/1702.07521/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2017.

## Built on by (most cited first, 15 of 15)

- [CrowdGuard: Federated Backdoor Detection in Federated Learning](https://mlsanity.com/paper/2210.07714/) (2022)
- [DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware](https://mlsanity.com/paper/2207.00083/) (2022)
- [secureTF: A Secure TensorFlow Framework](https://mlsanity.com/paper/2101.08204/) (2021)
- [MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers](https://mlsanity.com/paper/2109.12550/) (2021)
- [Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective](https://mlsanity.com/paper/2111.03308/) (2021)
- [OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification](https://mlsanity.com/paper/2202.07165/) (2022)
- [Ownership Verification of DNN Architectures via Hardware Cache Side Channels](https://mlsanity.com/paper/2102.03523/) (2021)
- [Edge Security: Challenges and Issues](https://mlsanity.com/paper/2206.07164/) (2022)
- [Activation Functions Considered Harmful: Recovering Neural Network Weights through Controlled Channels](https://mlsanity.com/paper/2503.19142/) (2025)
- [Automated Side Channel Analysis of Media Software with Manifold Learning](https://mlsanity.com/paper/2112.04947/) (2021)
- [How to Securely Shuffle? A survey about Secure Shufflers for privacy-preserving computations](https://mlsanity.com/paper/2507.01487/) (2025)
- [How To Think About End-To-End Encryption and AI: Training, Processing, Disclosure, and Consent](https://mlsanity.com/paper/2412.20231/) (2024)
- [Health+: Empowering Individuals via Unifying Health Data](https://mlsanity.com/paper/2602.19319/) (2026)
- [RobPI: Robust Private Inference against Malicious Client](https://mlsanity.com/paper/2602.19918/) (2026)
- [Downsides of Smartness Across Edge-Cloud Continuum in Modern Industry](https://mlsanity.com/paper/2603.29289/) (2026)

## 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.