The Effect of Length on Key Fingerprint Verification Security and Usability
Bibliographic record
Abstract
In applications such as end-to-end encrypted instant messaging, secure email, and device pairing, users need to compare key fingerprints to detect impersonation and adversary-in-the-middle attacks. Key fingerprints are usually computed as truncated hashes of each party’s view of the channel keys, encoded as an alphanumeric or numeric string, and compared out-of-band, e.g. manually, to detect any inconsistencies. Previous work has extensively studied the usability of various verification strategies and encoding formats, however, the exact effect of key fingerprint length on the security and usability of key fingerprint verification has not been rigorously investigated. We present a 162-participant study on the effect of numeric key fingerprint length on comparison time and error rate. While the results confirm some widely-held intuitions such as general comparison times and errors increasing significantly with length, a closer look reveals interesting nuances. The significant rise in comparison time only occurs when highly similar fingerprints are compared, and comparison time remains relatively constant otherwise. On errors, our results clearly distinguish between security non-critical errors that remain low irrespective of length and security critical errors that significantly rise, especially at higher fingerprint lengths. A noteworthy implication of this latter result is that Signal / WhatsApp key fingerprints provide a considerably lower level of security than usually assumed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".