XSnare: Application-specific client-side cross-site scripting protection
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
We present XSnare, a client-side Cross-Site Scripting (XSS) solution implemented as a Firefox extension. The client-side design of XSnare can protect users before application developers release patches and before server operators apply them.XSnare blocks XSS attacks by using previous knowledge of a web application’s HTML template content and the rich DOM context. XSnare uses a database of exploit descriptions, which are written with the help of previously recorded CVEs. It singles out injection points for exploits in the HTML and dynamically sanitizes content to prevent malicious payloads from appearing in the DOM. XSnare displays a secured version of the site, even if is exploited.We evaluated XSnare on 81 recent CVEs related to XSS attacks, and found that it defends against 93.8% of these exploits. To the best of our knowledge, XSnare is the first protection mechanism for XSS that is application-specific, and based on publicly available CVE information. We show that XSnare’s specificity protects users against exploits which evade other, more generic, XSS defenses.Our performance evaluation shows that our extension’s overhead on web page loading time is less than 10% for 72.6% of the sites in the Moz Top 500 list.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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