Alias-Aware Propagation of Simple Pattern-Based Properties in PHP Applications
Bibliographic record
Abstract
In this paper, we present novel algorithms for the propagation of pattern-based properties in PHP applications. Intuitively, pattern-based properties designate those properties that are intrinsically associated to syntactic patterns in the source code. Security checks in access control models are an example of pattern-based properties. At the source code level, permissions are typically verified with stereotyped constructs, called security checks, that can be detected with syntactic patterns. Depending on the program, pattern-based properties can be a liased to variables that are propagated through the application. In that context, support from data-flow approaches is needed to track the propagation of patterns through the application. In the context of this paper, we focus on the alias-aware propagation of security checks through PHP applications. Specifically, we investigated the propagation of security checks in 8 PHP applications that implement access control models. We show how, using the Data log language, one can implement conceptually complex data-flow algorithms in an incremental, intuitive and compact manner. From the results perspective, we show how our algorithm identifies security checks and security check a liased variables in a precise way. The reported false positive rate varies between 0% and 4% for the investigated applications.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".