Secure Processors Part II: Intel SGX Security Analysis and MIT Sanctum Architecture
Bibliographic record
Abstract
This manuscript is the second in a two part survey and analysis of the state of the art in secure processor systems, with a specific focus on remote software attestation and software isolation. The first part established the taxonomy and prerequisite concepts relevant to an examination of the state of the art in trusted remote computation: attested software isolation containers (enclaves). This second part extends Part I’s description of Intel’s Software Guard Extensions (SGX), an available and documented enclave-capable system, with a rigorous security analysis of SGX as a system for trusted remote computation. This part documents the authors’ concerns over the shortcomings of SGX as a secure system and introduces the MIT Sanctum processor developed by the authors: a system designed to offer stronger security guarantees, lend itself better to analysis and formal verification, and offer a more straightforward and complete threat model than the Intel system, all with an equivalent programming model. This two part work advocates a principled, transparent, and well-scrutinized approach to system design, and argues that practical guarantees of privacy and integrity for remote computation are achievable at a reasonable design cost and performance overhead.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".