Breaking Darknet CAPTCHAs with general purpose LLM
Our work evaluates the effectiveness of automated methods for solving CAPTCHA challenges commonly encountered in darknet environments. These CAPTCHAs are typically designed to operate without JavaScript, resulting in distinct characteristics compared to mainstream CAPTCHA systems. Our study considers three representative challenge types: open-circle localization, rotation-based alignment, and object-selection CAPTCHAs. The experiments reveal a systematic limitation of contemporary MLLMs: while they are generally capable of identifying relevant visual structures, they frequently struggle with precise spatial localization and geometric transformations. These deficiencies can be mitigated ei...
| Authors | Benjamin Fehrensen, Jens Hubler |
|---|---|
| Primary category | cs.CR |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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