SQL-Injection Security Evolution Analysis in PHP
Bibliographic record
Abstract
Web sites are often a mixture of static sites and programs that integrate relational databases as a back-end. Software that implements Web sites continuously evolve to meet ever-changing user needs. As a Web sites evolve, new versions of programs, interactions and functionalities are added and existing ones are removed or modified. Web sites require configuration and programming attention to assure security, confidentiality, and trustiness of the published information. During evolution of Web software, from one version to the next one, security flaws may be introduced, corrected, or ignored. This paper presents an investigation of the evolution of security vulnerabilities as detected by propagating and combining granted authorization levels along an inter-procedural control flow graph (CFG) together with required security levels for DB accesses with respect to SQL-injection attacks. The paper reports results about experiments performed on 31 versions of phpBB, that is a publicly available bulletin board written in PHP, version 1.0.0 (9547 LOC) to version 2.0.22 (40663 LOC) have been considered as a case study. Results show that the vulnerability analysis can be used to observe and monitor the evolution of security vulnerabilities in subsequent versions of the same software package. Suggestions for further research are also presented.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".