SQLPrevent: Eective Dynamic Detection and Prevention of SQL Injection Attacks Without Access to the Application Source Code
Bibliographic record
Abstract
This paper presents an effective approach for detecting and preventing known as well as novel SQL injection attacks. Unlike existing approaches, ours (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 need training traces, (4) does not require modification of the runtime environment, such as PHP interpreter or JVM, and (5) is independent of the back-end database used. Our approach is based on two simple observations, that (1) in malicious HTTP requests, parameter values are used not only as literals in the corresponding SQL statements but also as other SQL constructs, such as delimiters, identifiers or operators; and (2) a malformed parameter value in an HTTP request comprises more than one SQL token. We use J2EE to implement a tool we have named SQLPrevent that dynamically detects SQL injection attacks using the above heuristics, and blocks the corresponding SQL statements from being submitted to the back-end database. Using the AMNESIA testbed, we evaluate SQLPrevent over 15,000 unique HTTP requests with five web applications. In our experiments, SQLPrevent produced no false positives or false negatives, and imposed at most 4% (0.3% on average) performance overhead with respect to average 500 millisecond response time in the testbed applications.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 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".