SgxPectre Attacks: Stealing Intel Secrets from SGX Enclaves via Speculative Execution
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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.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2018.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 3; 2024: 3; 2025: 1; 2026: 3.
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- 2023No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device MLcited by 21
- 2021secureTF: A Secure TensorFlow Frameworkcited by 12
- 2022OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsificationcited by 4
- 2024TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Modelscited by 3
- 2021Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Supportcited by 2
- 2023Hyperdimensional Computing as a Rescue for Efficient Privacy-Preserving Machine Learning-as-a-Servicecited by 2
- 2024Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models (Extended Version)cited by 2
- 2023Integrating Homomorphic Encryption and Trusted Execution Technology for Autonomous and Confidential Model Refining in Cloudcited by 0
- 2024Props for Machine-Learning Securitycited by 0
- 2025SecONNds: Secure Outsourced Neural Network Inference on ImageNetcited by 0
- 2026Supercharging Federated Intelligence Retrievalcited by 0
- 2026TrEEStealer: Stealing Decision Trees via Enclave Side Channelscited by 0
1 more
- 2026When Agents Handle Secrets: A Survey of Confidential Computing for Agentic AI4 May 2026
- 2026TrEEStealer: Stealing Decision Trees via Enclave Side Channels20 Apr 2026
- 2026Supercharging Federated Intelligence Retrieval26 Mar 2026
- 2025SecONNds: Secure Outsourced Neural Network Inference on ImageNet13 Jun 2025
- 2024TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models15 Nov 2024
- 2024Props for Machine-Learning Security27 Oct 2024
- 2024Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models (Extended Version)11 Jul 2024
- 2023No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML11 Oct 2023
- 2023Hyperdimensional Computing as a Rescue for Efficient Privacy-Preserving Machine Learning-as-a-Service17 Aug 2023
- 2023Integrating Homomorphic Encryption and Trusted Execution Technology for Autonomous and Confidential Model Refining in Cloud2 Aug 2023
- 2022OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification15 Feb 2022
- 2021Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Support31 Mar 2021
1 more
- 2021secureTF: A Secure TensorFlow Framework20 Jan 2021
- 2023No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device MLcited by 21
- 2021secureTF: A Secure TensorFlow Frameworkcited by 12
- 2022OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsificationcited by 4
- 2024TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Modelscited by 3
- 2021Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Supportcited by 2
- 2023Hyperdimensional Computing as a Rescue for Efficient Privacy-Preserving Machine Learning-as-a-Servicecited by 2
- 2024Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models (Extended Version)cited by 2
- 2023Integrating Homomorphic Encryption and Trusted Execution Technology for Autonomous and Confidential Model Refining in Cloudcited by 0
- 2024Props for Machine-Learning Securitycited by 0
- 2025SecONNds: Secure Outsourced Neural Network Inference on ImageNetcited by 0
- 2026Supercharging Federated Intelligence Retrievalcited by 0
- 2026TrEEStealer: Stealing Decision Trees via Enclave Side Channelscited by 0
- 2026TrEEStealer: Stealing Decision Trees via Enclave Side Channels20 Apr 2026
- 2026Supercharging Federated Intelligence Retrieval26 Mar 2026
- 2025SecONNds: Secure Outsourced Neural Network Inference on ImageNet13 Jun 2025
- 2024TEESlice: Protecting Sensitive Neural Network Models in Trusted Execution Environments When Attackers have Pre-Trained Models15 Nov 2024
- 2024Props for Machine-Learning Security27 Oct 2024
- 2024Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models (Extended Version)11 Jul 2024
- 2023No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML11 Oct 2023
- 2023Hyperdimensional Computing as a Rescue for Efficient Privacy-Preserving Machine Learning-as-a-Service17 Aug 2023
- 2023Integrating Homomorphic Encryption and Trusted Execution Technology for Autonomous and Confidential Model Refining in Cloud2 Aug 2023
- 2022OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification15 Feb 2022
- 2021Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Support31 Mar 2021
- 2021secureTF: A Secure TensorFlow Framework20 Jan 2021