SoK: SGX.Fail: How Stuff Gets eXposed
Abstract
Intel’s Software Guard Extensions (SGX) promises an isolated execution environment, protected from all software running on the machine. As such, numerous works have sought to leverage SGX to provide confidentiality and integrity guarantees for code running in adversarial environments. In the past few years however, SGX has come under heavy fire, threatened by numerous hardware attacks. With Intel repeatedly patching SGX to regain security while consistently launching new (micro)architectures, it is increasingly difficult to track the applicability of various attack techniques across the SGX design landscape.Thus, in this paper we set out to survey and categorize various SGX attacks, their applicability to different SGX architectures, as well as the information leaked by them. We then set out to explore the effectiveness of SGX’s update mechanisms in preventing attacks on real-world deployments. Here, we study two commercial SGX applications. First, we investigate the SECRET network, an SGX-backed blockchain aiming to provide privacy-preserving smart contracts. Next, we also consider PowerDVD, a UHD Blu-Ray Digital Rights Management (DRM) software licensed to play discs on PCs. We show that in both cases vendors are unable to meet security goals originally envisioned for their products, presumably due to SGX’s long update timelines and the complexities of a manual update process. This in turn forces vendors to make difficult security/usability trade offs, resulting in security…
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
TEEs are inherently hardware-dependent and may be vulnerable to microarchitectural attacks.
“TEE are inherently hardware-dependent and may be vulnerable to microarchitectural attacks [38], [39], [40], [41], [42], [43].”
From Hollow-LLM Attack: Computationally Trivial Weights in Zero-Knowledge Verification of LLM Inference (2026) · cites this paper as [38]
Cited by
Sorted by citations
Hollow-LLM Attack: Computationally Trivial Weights in Zero-Knowledge Verification of LLM Inference
Gong, C., Liu, B., & Li, M. (2026) · 2026 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp63933.2026.00258
- Authors
- Van Schaik, S., Seto, A., Yurek, T., Batori, A., AlBassam, B., Genkin, D., Miller, A., Ronen, E., Yarom, Y., & Garman, C.
- Affiliations
- University of Michigan
- Purdue University West Lafayette (US)
- University of Illinois Urbana-Champaign (US)
- University of Michigan (US)
- Georgia Institute of Technology (US)
- Tel Aviv University (IL)
- Ruhr University Bochum (DE)
- Venue
- 2024 IEEE Symposium on Security and Privacy (SP)
- Publisher
- IEEE
- Published
- 2024-05-19
- Pages
- 4143-4162
- ISSN
- 1081-6011 (ISSN-L)
- OpenAlex
- W4402263683
- Cited by
- 31
- References
- 0
- Citations by year
- 2026: 8 · 2025: 19 · 2024: 3 · 2023: 1
- FWCI
- 8.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Symposium on Security and Privacy (S&P) (publishedVersion)
- Funders
- Air Force Office of Scientific Research
- Deutsche Forschungsgemeinschaft
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Security and Verification in Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptographic Implementations and Security
- Physical Sciences › Computer Science › Information Systems › Digital Rights Management and Security
- Keywords
- Intel SGX, hardware attacks, trusted execution environments, attack classification, secret networks, blockchain security, smart contracts, digital rights management, security-usability trade-off, information leakage
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review