All your Google and Facebook logins are belong to us: A case for single sign-off
Bibliographic record
Abstract
The websites of the modern Web integrate content from multiple parties to provide an enriched user experience. The so-called single sign-on forms part of this integration whereby a relying website enables a user to use her credentials on a third-party provider (such as Google or Facebook) to authenticate with itself and, if desired, authorize itself to use her resources on the provider. The user benefits by not remembering credentials for different websites separately and by allowing controlled use of her resources with a provider by other website. However, we observe that the current protocols for single sign-on do not have any provision of what we call single sign-off: once the user logs out of a relying website, the user still remains signed into the provider website. This can leave the user vulnerable if she forgets to sign out of the provider website after signing out of the relying website on a shared computer. We manually analyze the top twenty websites using Facebook or Google providers and conclude that the above problem is widespread. All but one website do not even warn the user with regard to this problem.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".