Rethinking the Use of Resource Hints in HTML5: Is Faster Always Better!?
Bibliographic record
Abstract
To date, much of the development in Web-related technologies has been driven by the users’ quest for ever faster and more intuitive WWW. One of the most recent trends in this development is built around the idea that a user’s WWW experience can further be improved by predicting and/or preloading Web resources that are likely sought by the user, ahead of time. Resource hints is a set of features introduced in HTML5 and intended to support the idea of predictive preloading in the WWW. Inspite of the fact that resource hints were originally intended to enhance the online user experience, their introduction has unfortunately created a vulnerability that can be exploited to attack the user’s privacy, security and reputation, or to turn the user’s computer into a bot that can compromise the integrity of business analytics. In this article we outline six different scenarios (i.e., attacks) in which the resource hints could end up turning the browser into a dangerous tool that acts without the knowledge of and/or against its very own user. What makes these attacks particularly concerning is the fact that they are extremely easy to execute, and they do not require that any form of client-side malware be implanted on the user machine. While one of the attacks is (just) a new form of the well-known cross-site request forgery attacks, the other attacks have not been addressed much or at all in the research literature. Through this work, we ultimate hope to make the wider Internet community critically rethink the way the resource hints are implemented and used in today’sWWW.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.021 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".