SoK: Software Compartmentalization
Bibliographic record
Abstract
Decomposing large systems into smaller components with limited privileges has long been recognized as an effective means to minimize the impact of exploits. Despite historical roots, demonstrated benefits, and a plethora of research efforts in academia and industry, the compartmentalization of software is still not a mainstream practice. This paper investigates why, and how this status quo can be improved. Noting that existing approaches are fraught with inconsistencies in terminology and analytical methods, we propose a unified model for the systematic analysis, comparison, and directing of compartmentalization approaches. We use this model to review 211 research efforts and analyze 61 mainstream compartmentalized systems, confronting them to understand the limitations of both research and production works. Among others, our findings reveal that mainstream efforts largely rely on manual methods, custom abstractions, and legacy mechanisms, poles apart from recent research. We conclude with recommendations: compartmentalization should be solved holistically; progress is needed towards simplifying the definition of compartmentalization policies; towards better challenging our threat models in the light of confused deputies and hardware limitations; as well as towards bridging the gaps we pinpoint between research and mainstream needs. This paper not only maps the historical and current landscape of compartmentalization, but also sets forth a framework to foster their evolution and adoption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.020 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".