Evaluating the Strength and Availability of Multilingual Passphrase Authentication
Bibliographic record
Abstract
Passwords are a ubiquitous form of authentication that is still present for many online services and platforms.Researchers have measured password creation policies for a multitude of websites and studied password creation behaviour for users who speak various languages.Evidence shows that limiting all users to alphanumeric characters and select special characters resulted in weaker passwords for certain demographics.However, password creation policies still concentrate on only alphanumeric characters and focus on increasing the length of passwords rather than the diversity of potential characters in the password.With the recent recommendation towards passphrases, further concerns arise pertaining to the potential consequences of not being inclusive in password creation.Previous work studying multilingual passphrase policies that combined English and African languages showed that multilingual passphrases are more user-friendly and also more difficult to guess than a passphrase based on a single language.However, their work only studied passphrases based on standard alphanumeric characters.In this paper, we measure the password strength of using a multilingual passphrase that contains characters outside of the standard alphanumeric characters and assess the availability of such multilingual passwords for websites with free account creation in the Tranco top 50 list and the Semrush top 20 websites in China list.We find that password strength meters like zxcvbn and MultiPSM surprisingly struggle with correctly assessing the strength of non-English-only passphrases with MultiPSM encountering an encoding issue with non-alphanumeric characters.In addition, we find that half of all tested valid websites accept multilingual passphrases but three websites struggled in general due to imposing a maximum password character limitation.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".