BLACKOUT: Data-Oblivious Computation with Blinded Capabilities
Bibliographic record
Abstract
Lack of memory-safety and exposure to side channels are two prominent, persistent challenges for the secure implementation of software. Memory-safe programming languages promise to significantly reduce the prevalence of memory-safety bugs, but make it more difficult to implement side-channel-resistant code. We aim to address both memory-safety and side-channel resistance by augmenting memory-safe hardware with the ability for data-oblivious programming. We describe an extension to the CHERI capability architecture to provide blinded capabilities that allow data-oblivious computation to be carried out by userspace tasks. We also present BLACKOUT, our realization of blinded capabilities on a FPGA softcore based on the speculative out-of-order CHERI-Toooba processor and extend the CHERI-enabled Clang/LLVM compiler and the CheriBSD operating system with support for blinded capabilities. BLACKOUT makes writing side-channel-resistant code easier by making non-data-oblivious operations via blinded capabilities explicitly fault. Through rigorous evaluation we show that BLACKOUT ensures memory operated on through blinded capabilities is securely allocated, used, and reclaimed and demonstrate that, in benchmarks comparable to those used by previous work, BLACKOUT imposes only a small performance degradation (1.5% geometric mean) compared to the baseline CHERI-Toooba processor.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".