An administrator's guide to internet password research
Bibliographic record
Abstract
The research literature on passwords is rich but little of it directly aids those charged with securing web-facing services or setting policies. With a view to improving this situation we examine questions of implementation choices, policy and administration using a combination of literature survey and first-principles reasoning to identify what works, what does not work, and what remains unknown. Some of our results are surprising. We find that offline attacks, the justification for great demands of user effort, occur in much more limited circumstances than is generally believed (and in only a minority of recently-reported breaches). We find that an enormous gap exists between the effort needed to withstand online and offline attacks, with probable safety occurring when a password can survive 106 and 1014 guesses respectively. In this gap, eight orders of magnitude wide, there is little return on user effort: exceeding the online threshold but falling short of the offline one represents wasted effort. We find that guessing resistance above the online threshold is also wasted at sites that store passwords in plaintext or reversibly encrypted: there is no attack scenario where the extra effort protects the account.
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 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 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.240 | 0.267 |
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".