Password Guessers Under a Microscope: An In-Depth Analysis to Inform\n Deployments
Bibliographic record
Abstract
Password guessers are instrumental for assessing the strength of passwords.\nDespite their diversity and abundance, little is known about how different\nguessers compare to each other. We perform in-depth analyses and comparisons of\nthe guessing abilities and behavior of password guessers. To extend analyses\nbeyond number of passwords cracked, we devise an analytical framework to\ncompare the types of passwords that guessers generate under various conditions\n(e.g., limited training data, limited number of guesses, and dissimilar\ntraining and target data). Our results show that guessers often produce\ndissimilar guesses, even when trained on the same data. We leverage this result\nto show that combinations of computationally-cheap guessers are as effective as\ncomputationally intensive guessers, but more efficient. Our insights allow us\nto provide a concrete set of recommendations for system administrators when\nperforming password checking.\n
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".