Semantic smells and errors in access control models: A case study in PHP
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Access control models implement mechanisms to restrict access to sensitive data from unprivileged users. Access controls typically check privileges that capture the semantics of the operations they protect. Semantic smells and errors in access control models stem from privileges that are partially or totally unrelated to the action they protect. This paper presents a novel approach, partly based on static analysis and information retrieval techniques, for the automatic detection of semantic smells and errors in access control models. Investigation of the case study application revealed 31 smells and 2 errors. Errors were reported to developers who quickly confirmed their relevance and took actions to correct them. Based on the obtained results, we also propose three categories of semantic smells and errors to lay the foundations for further research on access control smells in other systems and domains.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it