Control Flow Based Pointcuts for Security Hardening Concerns
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we present two new control flow based point-cuts to Aspect-Oriented Programming (AOP) languages that are needed for systematic hardening of security concerns. They allow to identify particular join points in a program’s control flow graph (CFG). The first proposed primitive is the GAFlow, the closest guaranteed ancestor, which returns the closest ancestor join point to the pointcuts of interest that is on all their runtime paths. The second proposed primitive is the GDFlow, the closest guaranteed descendant, which returns the closest child join point that can be reached by all paths starting from the pointcuts of interest. We find these pointcuts to be necessary because they are needed to perform many security hardening practices and, to the best of our knowledge, none of the existing pointcuts can provide their functionalities. Moreover, we show the viability and correctness of our proposed pointcuts by elaborating and implementing their algorithms and presenting the results of a testing case study.
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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 | 0.001 | 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 | 0.001 | 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