MUSIC: Mutation-based SQL Injection Vulnerability Checking
Bibliographic record
Abstract
SQL injection is one of the most prominent vulnerabilities for web-based applications. Exploitation of SQL injection vulnerabilities (SQLIV) through successful attacks might result in severe consequences such as authentication bypassing, leaking of private information etc. Therefore, testing an application for SQLIV is an important step for ensuring its quality. However, it is challenging as the sources of SQLIV vary widely, which include the lack of effective input filters in applications, insecure coding by programmers, inappropriate usage of APIs for manipulating databases etc. Moreover, existing testing approaches do not address the issue of generating adequate test data sets that can detect SQLIV. In this work, we present a mutation-based testing approach for SQLIV testing. We propose nine mutation operators that inject SQLIV in application source code. The operators result in mutants, which can be killed only with test data containing SQL injection attacks. By this approach, we force the generation of an adequate test data set containing effective test cases capable of revealing SQLIV. We implement a MUtation-based SQL Injection vulnerabilities Checking (testing) tool (MUSIC) that automatically generates mutants for the applications written in Java Server Pages (JSP) and performs mutation analysis. We validate the proposed operators with five open source web-based applications written in JSP. We show that the proposed operators are effective for testing SQLIV.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".