S2XS2: A Server Side Approach to Automatically Detect XSS Attacks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cross site scripting (XSS) vulnerabilities are widespread in web-based programs. Server side detection of suspected contents can mitigate XSS exploitations early. Unfortunately, existing serve side approaches impose modification of server and client side environments. In this paper, we develop an automated framework to detect XSS attacks at the server side based on the notion of boundary injection and policy generation. Boundaries mark content generation locations in server script code. We derive expected benign features of dynamic contents that are matched during response page generation to detect attacks. We develop a prototype tool to automatically insert boundaries and generate policies for JSP programs. We evaluate the approach with four JSP programs. The results indicate that the approach detects most of the well known XSS attacks. Moreover, the false positive rates vary between zero and 5.2%. The approach suffers from negligible runtime overhead.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it