Bibliographic record
Abstract
Millions of web users today employ their Facebook accounts to sign into more than one million relying party (RP) websites. This web-based single sign-on (SSO) scheme is enabled by OAuth 2.0, a web resource authorization protocol that has been adopted by major service providers. The OAuth 2.0 protocol has proven secure by several formal methods, but whether it is indeed secure in practice remains an open question. We examine the implementations of three major OAuth identity providers (IdP) (Facebook, Microsoft, and Google) and 96 popular RP websites that support the use of Facebook accounts for login. Our results uncover several critical vulnerabilities that allow an attacker to gain unauthorized access to the victim user's profile and social graph, and impersonate the victim on the RP website. Closer examination reveals that these vulnerabilities are caused by a set of design decisions that trade security for implementation simplicity. To improve the security of OAuth 2.0 SSO systems in real-world settings, we suggest simple and practical improvements to the design and implementation of IdPs and RPs that can be adopted gradually by individual sites.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.215 | 0.167 |
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".