An Analysis of Effectiveness of Black-Box Web Application Scanners in Detection of Stored SQL Injection and Stored XSS Vulnerabilities
Bibliographic record
Abstract
Black-box web application scanners are used to detect vulnerabilities in the web application without any knowledge of the source code. Recent research had shown their poor performance in detecting stored Cross-Site Scripting (XSS) and stored SQL Injection (SQLI). The detection efficiency of four black-box scanners on two testbeds, Wackopicko and Custom testbed Scanit (obtained from [5]), have been analyzed in this paper. The analysis showed that the scanners need to be improved for better detection of multi-step stored XSS and stored SQLI. This study involves the interaction between the selected scanners and the web application to measure their efficiency of inserting proper attack vectors in appropriate fields. The results of this research paper indicate that there is not much difference in terms of performance between open-source and commercial black-box scanners used in this research. However, it may depend on the policies and trust issues of the companies using them according to their needs. Some of the possible recommendations are provided to improve the detection rate of stored SQLI and stored XSS vulnerabilities in this paper. The study concludes that the state-of-the-art of automated black-box web application scanners in 2020 needs to be improved to detect stored XSS and stored SQLI more effectively.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".