# SgxPectre Attacks: Stealing Intel Secrets from SGX Enclaves via Speculative Execution

arXiv: https://arxiv.org/abs/1802.09085 · HTML: https://mlsanity.com/paper/1802.09085/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2018.

## Built on by (most cited first, 14 of 14)

- [No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML](https://mlsanity.com/paper/2310.07152/) (2023)
- [Proof of Unlearning: Definitions and Instantiation](https://mlsanity.com/paper/2210.11334/) (2022)
- [secureTF: A Secure TensorFlow Framework](https://mlsanity.com/paper/2101.08204/) (2021)
- [OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification](https://mlsanity.com/paper/2202.07165/) (2022)
- [TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models](https://mlsanity.com/paper/2411.09945/) (2024)
- [Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Support](https://mlsanity.com/paper/2103.16898/) (2021)
- [Hyperdimensional Computing as a Rescue for Efficient Privacy-Preserving Machine Learning-as-a-Service](https://mlsanity.com/paper/2310.06840/) (2023)
- [Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models (Extended Version)](https://mlsanity.com/paper/2407.08152/) (2024)
- [Props for Machine-Learning Security](https://mlsanity.com/paper/2410.20522/) (2024)
- [Integrating Homomorphic Encryption and Trusted Execution Technology for Autonomous and Confidential Model Refining in Cloud](https://mlsanity.com/paper/2308.00963/) (2023)
- [SecONNds: Secure Outsourced Neural Network Inference on ImageNet](https://mlsanity.com/paper/2506.11586/) (2025)
- [Supercharging Federated Intelligence Retrieval](https://mlsanity.com/paper/2603.25374/) (2026)
- [TrEEStealer: Stealing Decision Trees via Enclave Side Channels](https://mlsanity.com/paper/2604.18716/) (2026)
- [When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AI](https://mlsanity.com/paper/2605.03213/) (2026)

## Abstract (arXiv)

This paper presents SgxPectre Attacks that exploit the recently disclosed CPU bugs to subvert the confidentiality and integrity of SGX enclaves. Particularly, we show that when branch prediction of the enclave code can be influenced by programs outside the enclave, the control flow of the enclave program can be temporarily altered to execute instructions that lead to observable cache-state changes. An adversary observing such changes can learn secrets inside the enclave memory or its internal registers, thus completely defeating the confidentiality guarantee offered by SGX. To demonstrate the practicality of our SgxPectre Attacks, we have systematically explored the possible attack vectors of branch target injection, approaches to win the race condition during enclave's speculative execution, and techniques to automatically search for code patterns required for launching the attacks. Our study suggests that any enclave program could be vulnerable to SgxPectre Attacks since the desired code patterns are available in most SGX runtimes (e.g., Intel SGX SDK, Rust-SGX, and Graphene-SGX). Most importantly, we have applied SgxPectre Attacks to steal seal keys and attestation keys from Intel signed quoting enclaves. The seal key can be used to decrypt sealed storage outside the enclaves and forge valid sealed data; the attestation key can be used to forge attestation signatures. For these reasons, SgxPectre Attacks practically defeat SGX's security protection. This paper also systematically evaluates Intel's existing countermeasures against SgxPectre Attacks and discusses the security implications.