Injecting Comments to Detect JavaScript Code Injection Attacks
Bibliographic record
Abstract
Most web programs are vulnerable to cross site scripting (XSS) that can be exploited by injecting JavaScript code. Unfortunately, injected JavaScript code is difficult to distinguish from the legitimate code at the client side. Given that, server side detection of injected JavaScript code can be a layer of defense. Existing server side approaches rely on identifying legitimate script code, and an attacker can circumvent the technique by injecting legitimate JavaScript code. Moreover, these approaches assume that no JavaScript code is downloaded from third party websites. To address these limitations, we develop a server side approach that distinguishes injected JavaScript code from legitimate JavaScript code. Our approach is based on the concept of injecting comment statements containing random tokens and features of legitimate JavaScript code. When a response page is generated, JavaScript code without or incorrect comment is considered as injected code. Moreover, the valid comments are checked for duplicity. Any presence of duplicate comments or a mismatch between expected code features and actually observed features represents JavaScript code as injected. We implement a prototype tool that automatically injects JavaScript comments and deploy injected JavaScript code detector as a server side filter. We evaluate our approach with three JSP programs. The evaluation results indicate that our approach detects a wide range of code injection attacks.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.002 |
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".