Testing and assessing web vulnerability scanners for persistent SQL injection attacks
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
Web application security scanners are automated tools used to detect security vulnerabilities in web applications. Recent research has shown that detecting persistent SQL injection vulnerabilities, one of the most critical web application vulnerabilities, is a major challenge for black-box scanners. In this paper, we evaluate three state of art black-box scanners that support detecting persistent SQL injection vulnerabilities. We developed our custom testbed "MatchIt" that tests the scanners capability in detecting persistent SQL injections. The results show that existing vulnerabilities are not detected even when these automated scanners are explicitly configured to exploit the vulnerability. The weaknesses of blackbox scanners identified reside in many areas: crawling web pages, input values and attack code selection, user registration and login, analysis of server replies and classification of findings. Because of the poor detection rate, we analyze the scanner's behavior and present a set of recommendations that could enhance the discovery of persistent SQL injection vulnerabilities.
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 | 0.001 | 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 | 0.001 |
| 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