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.
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SyML: Guiding Symbolic Execution Toward Vulnerable States Through Pattern Learning
Exploring many execution paths in a binary program is essential to discover new vulnerabilities. Dynamic Symbolic Execution (DSE) is useful to trigger complex input conditions and enables an accurate exploration of a program while providing extensive crash replayability and semantic insights.
However, scaling this type of analysis to complex binaries is difficult. Current methods suffer from the path explosion problem, despite many attempts to mitigate this challenge (e.g., by merging paths when appropriate). Still, in general, this challenge is not yet surmounted, and most bugs discovered through such techniques are shallow.
We propose a novel approach to address the path explosion problem: A smart triaging system that leverages supervised machine learning techniques to replicate human expertise, leading to vulnerable path discovery. Our approach monitors the execution traces in vulnerable programs and extracts relevant features—register and memory accesses, function complexity, system calls—to guide the symbolic exploration. We train models to learn the patterns of vulnerable paths from the extracted features, and we leverage their predictions to discover interesting execution paths in new programs.
We implement our approach in a tool called SyML, and we evaluate it on the Cyber Grand Challenge (CGC) dataset—a well-known dataset of vulnerable programs—and on 3 real-world Linux binaries. We show that the knowledge collected from the analysis of vulnerable paths, without any explicit prior knowledge about vulnerability patterns, is transferrable to unseen binaries, and leads to outperforming prior work in path prioritization by triggering more, and different, unique vulnerabilities.
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- Award ID(s):
- 1704253
- NSF-PAR ID:
- 10346374
- Date Published:
- Journal Name:
- RAID '21: 24th International Symposium on Research in Attacks, Intrusions and Defenses
- Page Range / eLocation ID:
- 456 to 468
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3471621.3471865
