Single Sign-On (SSO) and its Intersection with Phishing Attacks: An Investigation
Bibliographic record
Abstract
Users are increasingly prompted to click login links and login buttons from their emails and on websites, as services offer alternative login methods extending beyond traditional usernames and passwords. Single sign-on (SSO) simplifies password management by allowing users to login to services, like Spotify, Slack, Zoom, GitHub, Airbnb, and many more, using external identity providers (IDPs) like Google, Facebook, and Apple, to authenticate users using their already existing email address and accounts. We define a new phishing attack which is specifically targeted to SSO users, exploiting the “login with XYZ” button or link that takes the user to the malicious website. We then explore the possible consequences, specifically susceptibility to this new SSO-based phishing attack, questioning whether developing the habit of clicking on these buttons makes them disproportionately susceptible to this new type of phishing. To accomplish this, we created a user-study that included instances of SSO-based phishing.
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.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".