Insider and Ousider Threat-Sensitive SQL Injection Vulnerability Analysis 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
In general, SQL-injection attacks rely on some weak validation of textual input used to build database queries. Maliciously crafted input may threaten the confidentiality and the security policies of Web sites relying on a database to store and retrieve information. Furthermore, insiders may introduce malicious code in a Web application, code that, when triggered by some specific input, for example, would violate security policies. This paper presents an original approach based on static analysis to automatically detect statements in PHP applications that may be vulnerable to SQL-injections triggered by either malicious input (outsider threats) or malicious code (insider threats). Original flow analysis equations, that propagate and combine security levels along an inter-procedural control flow graph (CFG), are presented. The computation of security levels presents linear execution time and memory complexity
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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