SQLPrevent: Eective dynamic detection and prevention of SQL injection
Bibliographic record
Abstract
This paper presents an approach for retrofitting existing web applications with run-time protection against known as well as unseen SQL injection attacks (SQLIAs). This approach (1) is resistant to evasion techniques, such as hexadecimal encoding or inline comment, (2) does not require analysis or modification of the application source code, (3) does not require modification of the runtime environment, such as PHP interpreter or JVM, and (4) is independent of the back-end database used. The approach precision is also enhanced with a method for reducing the rate of false positives in the SQLIA detection logic via runtime discovery of the developers’ intention for individual SQL statements made by web applications. We have implemented the proposed approach in the form of protection mechanisms for J2EE applications. Named SQLPrevent, these mechanisms intercept both HTTP requests and SQL statements, mark and track parameter values originated from HTTP requests, and perform SQLIA detection and prevention on the intercepted SQL statements. We extended the AMNESIA testbed to contain false positive testing traces, and employed the extended testbed to evaluate SQLPrevent over 15,000 unique HTTP requests with five web applications. In our experiments, SQLPrevent produced no known false positives or false negatives, and imposed a 3.6% performance overhead with respect to 30 millisecond response time in the tested applications. We also ported SQLPrevent to ASP.NET and ASP, which is of vital importance to the protection of legacy ASP applications, as they have been the target of several massive SQLIAs since October 2007.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".