Bibliographic record
Abstract
A CAPTCHA is a special kind of AI hard test to prevent bots from logging into computer systems. We define an AI hard test to be a problem which is intractable for a computer to solve as a matter of general consensus of the AI community. On the Internet, CAPTCHAs are typically used to prevent bots from signing up for illegitimate e-mail accounts or to prevent ticket scalping on e-commerce web sites. We have found that a popular and distributed architecture for implementing CAPTCHAs used on the Internet has a flawed protocol. Consequently, the security that the CAPTCHA ought to provide does not work and is ineffective at keeping bots out. This paper discusses the flaw in the distributed architecture’s protocol. We propose an improved protocol while keeping the current architecture intact. We implemented a bot, which is 100% effective at breaking CAPTCHAs that use this flawed protocol. Furthermore, our implementation of the improved protocol proves that it is not vulnerable to attack. We use two popular web sites, tickets.com and youtube.com, to demonstrate our point.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".