Using Context-Based Password Strength Meter to Nudge Users' Password Generating Behavior: A Randomized Experiment
Why this work is in the frame
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Bibliographic record
Abstract
Encouraging users to create stronger passwords is one of the key issues in password-based authentication. It is particularly important as prior works have highlighted that most passwords are weak. Yet, passwords are still the most commonly used authentication method. This paper seeks to mitigate the issue of weak passwords by proposing a context-based password strength meter. We conduct a randomized experiment on Amazon MTurk and observe the change in users’ behavior. The results show that our proposed method is significantly effective. Users exposed to our password strength meter are more likely to change their passwords after seeing the warning message, and those new passwords are stronger. Furthermore, users are willing to invest their time to learn about creating a stronger password, even in a traditional password strength meter setting. Our findings suggest that simply incorporating contextual information to password strength meters could be an effective method in promoting more secure behaviors among end users.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.018 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it