Exploiting predictability in click-based graphical passwords
Why this work is in the frame
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Bibliographic record
Abstract
We provide an in-depth study of the security of click-based graphical password schemes like PassPoints (Weidenbeck et al., 2005), by exploring popular points (hot-spots), and examining strategies to predict and exploit them in guessing attacks. We report on both short- and long-term user studies: one lab-controlled, involving 43 users and 17 diverse images, the other a field test of 223 user accounts. We provide empirical evidence that hot-spots do exist for many images, some more so than others. We explore the use of “human-computation” (in this context, harvesting click-points from a small set of users) to predict these hot-spots. We generate two “human-seeded” attacks based on this method: one based on a first-order Markov model, another based on an independent probability model. Within 100 guesses, our first-order Markov model-based attack finds 4% of passwords in one image's data set, and 10% of passwords in a second image's data set. Our independent model-based attack finds 20% within 2 33 guesses in one image's data set and 36% within 2 31 guesses in a second image's data set. These are all for a system whose full password space has cardinality 2 43 . We evaluate our first-order Markov model-based attack with cross-validation of the field study data, which finds an average of 7–10% of user passwords within 3 guesses. We also begin to explore some click-order pattern attacks, which we found improve on our independent model-based attacks. Our results suggest that these graphical password schemes (with parameters as originally proposed) are vulnerable to offline and online attacks, even on systems that implement conservative lock-out policies.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it