uBOX: A Lightweight and Hardware-Assisted Sandbox for Multicore Embedded Systems
Bibliographic record
Abstract
Multicore embedded systems employ a big.LITTLE architecture to combine different cores into a single microcontroller (MCU). However, resources sharing among cores raises security challenges. Once LITTLE cores (which often receive external inputs) are compromised, the whole system will be affected. Existing hardware-assisted isolation approaches use privilege separation and code instrumentation to enforce memory isolation, which suffer from inefficiencies. This paper presentsuBOX, a lightweight sandbox for multicore embedded systems. The goal ofuBOXis to enforce memory isolation over untrusted software (on LITTLE cores) at the same privileged level. Specifically, it uses the Memory Protection Unit (MPU) to restrict memory access by untrusted software. To protect sandbox policies,uBOXdeprives the write capability of untrusted software towards MPU configurations by replacing its regular store instructions with unprivileged counterparts. Additionally, to protectuBOX's necessary regular store instructions from being abused,uBOX's memory is set to read-only and non-executable when running untrusted software. For the normal operation ofuBOX, we use an overlooked feature of the MPU and develop secure gates that quickly disable and re-enable the MPU, allowinguBOXto execute at a permissive memory view. Our evaluation demonstrates thatuBOXeffectively enforces isolation with average 1.27% runtime overhead, 0.83X Flash overhead, and 36.50X SRAM 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".