SgxPectre: Stealing Intel Secrets from SGX Enclaves Via Speculative Execution

By Guoxing Chen, Sanchuan Chen, Yuan Xiao, Yinqian Zhang, Zhiqiang Lin, Ten-Hwang Lai. European Symposium on Security and Privacy, 2018

383 citations recorded by the source corpus.

Speculative execution side-channel vulnerabilities in micro-architecture processors have raised concerns about the security of Intel SGX. To understand clearly the security impact of this vulnerability against SGX, this paper makes the following studies: First, to demonstrate the feasibility of the attacks, we present SgxPectre Attacks (the SGX-variants of Spectre attacks) that exploit speculative execution side-channel vulnerabilities to subvert the confidentiality of SGX enclaves. We show that when the 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. Second, to determine whether real-world enclave programs are impacted by the attacks, we develop techniques to automate the search of vulnerable code patterns in enclave binaries using symbolic execution. Our study suggests that nearly any enclave program could be vulnerable to SgxPectre Attacks since vulnerable code patterns are available in most SGX runtimes (e.g., Intel SGX SDK, Rust-SGX, and Graphene-SGX). Third, we apply 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. Finally, we evaluate Intel's existing countermeasures against SgxPectre Attacks and discusses the security implications.

Subfields: Artificial Intelligence, Hardware and Architecture, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5d82ad6d62776c0568c25645281ae7a1cf8b395c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jo Van Bulck
2. Frank Piessens
3. Murali Annavaram
4. Daniel Gruss
5. Yongqin Wang
6. Michael Schwarz
7. Stefan Mangard
8. Clémentine Maurice

In Hardware and Architecture

1. Murali Annavaram
2. Frank Piessens
3. Daniel Gruss
4. Yongqin Wang
5. Jo Van Bulck

In Signal Processing

1. Jo Van Bulck
2. Murali Annavaram
3. Frank Piessens
4. Daniel Gruss

Citations in the corpus

• Plundervolt: Software-based Fault Injection Attacks against Intel SGX (2020, background): “While there is a long line of work on dismantling SGX’s confidentiality guarantees [69, 11, 46, 73, 50, 25, 72] as well as exploiting classical memory safety vulnerabilities in enclaves [45, 8, 70], Plundervolt represents the first attack that directly violates SGX’s integrity guarantees for…”
• Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments (2019, methodology): “However, SGX has been shown to be prone to side channel attacks [3], [4] based on speculative execution bugs like Spectre and Meltdown [20], [22].”