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 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 | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".