Why this work is in the frame
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Bibliographic record
Abstract
Verifying that access-control systems maintain desired security properties is recognized as an important problem in security. Enterprise access-control systems have grown to protect tens of thousands of resources, and there is a need for verification to scale commensurately. We present techniques for abstraction-refinement and bound-estimation for bounded model checkers to automatically find errors in Administrative Role-Based Access Control (ARBAC) security policies. ARBAC is the first and most comprehensive administrative scheme for Role-Based Access Control (RBAC) systems. In the abstraction-refinement portion of our approach, we identify and discard roles that are unlikely to be relevant to the verification question (the abstraction step). We then restore such abstracted roles incrementally (the refinement steps). In the bound-estimation portion of our approach, we lower the estimate of the diameter of the reachability graph from the worst-case by recognizing relationships between roles and state-change rules. Our techniques complement one another, and are used with conventional bounded model checking. Our approach is sound and complete: an error is found if and only if it exists. We have implemented our technique in an access-control policy analysis tool called Mohawk . We show empirically that Mohawk scales well to realistic policies, and provide a comparison with prior tools.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it