"Lying makes me look suspicious": Users’ Perspectives and Analysis of Security Questions
Bibliographic record
Abstract
Despite recommendations from security authorities, such as those of the US NIST, against the use of security questions for online authentication, these methods are still used for login and account recovery processes. Although research on security questions has a long history, key gaps remain, particularly regarding user perceptions and the requirements used by websites for selecting and answering security questions. In this paper, we address these gaps through a two-part study: (i) a user survey (N = 292) capturing insights from a diverse US sample, and (ii) an analysis of an extensive set of 26 security requirements across 73 websites, totaling 1913 security questions. Our findings reveal notable user misconceptions, such as users’ believing that websites already possess correct answers to personal security questions. Additionally, We identify widespread insecure practices, such as accepting single characters and allowing identical answers across multiple security questions. By addressing both user perceptions and website security requirements, we provide a comprehensive understanding of weaknesses in current security question practices and contribute to the ongoing discourse on strengthening authentication methods.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".