Fool Me Once: A Study of Password Selection Evolution over the Past Decade
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Passwords have been around for many decades and have tenaciously remained the primary means of identification and authentication. Assuming that the communication channel is not intercepted, the strength of security provided by passwords is largely dependent on two factors: password selection and password storage mechanism. While both areas have been looked into by researchers in the past, there is no consensus to suggest whether or not humanity has moved towards choosing stronger passwords, notwithstanding strong password enforcement policies. One of the key reasons behind this shortcoming is the lack of data about individual credentials in leaked datasets, which usually contain only usernames and passwords. To the best of our knowledge, we are the first researchers to enrich the attribute set of any user credential database, thus allowing deeper insights. We outline the method we devised for adding new attributes (time-stamp and source inference) to a dataset of 1.4 billion user credentials. Subsequently, we use our modified dataset to determine how passwords have evolved overtime with respect to strength and whether humankind as a whole has learned from its past mistakes.
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.
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 it