MUTEC: Mutation-based testing of Cross Site Scripting
Bibliographic record
Abstract
Cross Site Scripting (XSS) is one of the worst vulnerabilities that allow malicious attacks such as cookie thefts and Web page defacements. Testing an implementation against XSS vulnerabilities (XSSVs) can avoid these consequences. Obtaining an adequate test data set is essential for testing of XSSVs. An adequate test data set contains effective test cases that can reveal XSSVs. Unfortunately, traditional testing techniques for XSSVs do not address the issue of adequate testing. In this work, we apply the idea of mutation-based testing technique to generate adequate test data sets for testing XSSVs. Our work addresses XSSVs related to Web-applications that use PHP and JavaScript code to generate dynamic HTML contents. We propose 11 mutation operators to force the generation of adequate test data set. A prototype mutation-based testing tool named MUTEC is developed to generate mutants automatically. The proposed operators are validated by using five open source applications having XSSVs. The results indicate that the proposed operators are effective for testing XSSVs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".