Property-testing real-world authorization systems
Bibliographic record
Abstract
We motivate and address the problem of testing for properties of interest in real-world implementations of authorization systems. We adopt a 4-stage process: (1) express a property precisely using existential second-order logic, (2) establish types of traces that are necessary and sufficient to establish a property, (3) adopt finitizing assumptions and show that under those assumptions, verifying a property is in PSPACE, and, (4) use a model-checker as a trace-generator to generate instances of traces, and exercise the implementation to check for those traces. We discuss our design of a corresponding testing-system, and its use to test for qualitatively different kinds of properties in two commercial authorization systems. One is a database system that we call the D system, and the other is a file-sharing system that we call the I system. (We use pseudonyms at the request of the respective vendors.) In the context of the D system, our testing has uncovered several issues with its authorization system in the context of procedures that aggregate SQL statements that, to our knowledge, are new to the research literature. For the I system, we have established that it possesses several properties of interest.
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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 | 0.015 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".