ENHANCING THE WORDPRESS SYSTEM:FROM ROLE TO ATTRIBUTE-BASED ACCESS CONTROL
Bibliographic record
Abstract
Role-Based Access Control (RBAC) is the most commonly used model on web applications. The advantages of RBAC are the ease of understanding, applying and managing privileges. The static RBAC model cannot alter access permission in real-time without human involvement and therefore the model suffers from increasing false negative (and/or false positive) outcomes. Hence, the Attribute-Based Access Control (ABAC) model has been proposed to introduce dynamicity and minimize human involvement in order to enhance security. WordPress is a very popular Role-Based content management system. To our best knowledge, no solution to merge from RBAC to ABAC model for WordPress applications has been found. Our contribution is a WordPress plug-in that we have developed to build ABAC upon the existing RBAC setups. In this journey, we have investigated various scenarios by studying different application categories to come up with an enhanced automatic model that adds real-time grant and revoke feature to WordPress.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".