Automated Protection of PHP Applications Against SQL-injection Attacks
Bibliographic record
Abstract
Web sites may be static sites, programs, or databases, and very often a combination of the three integrating relational databases as a back-end. Web sites require care in configuration and programming to assure security, confidentiality, and trustworthiness of the published information. SQL-injection attacks exploit 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. This paper presents an original approach that combines static analysis, dynamic analysis, and code re-engineering to automatically protect applications written in PHP from SQL-injection attacks. The paper also reports preliminary results of experiments performed on an old SQL-injection prone version of phpBB (version 2.0.0, 37193 LOC of PHP version 4.2.2 code). Results show that our approach successfully improved phpBB-2.0.0 resistance to SQL-injection attacks
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".