# Breaking reCAPTCHAv2

arXiv: https://arxiv.org/abs/2409.08831 · HTML: https://mlsanity.com/paper/2409.08831/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video](https://mlsanity.com/paper/1709.05943/) (2017)
- [Deep-CAPTCHA: a deep learning based CAPTCHA solver for vulnerability assessment](https://mlsanity.com/paper/2006.08296/) (2020)
- [An Object Detection based Solver for Google's Image reCAPTCHA v2](https://mlsanity.com/paper/2104.03366/) (2021)

## Built on by (most cited first, 13 of 13)

- [Frontier AI's Impact on the Cybersecurity Landscape](https://mlsanity.com/paper/2504.05408/) (2025)
- [Open CaptchaWorld: A Comprehensive Web-based Platform for Testing and Benchmarking Multimodal LLM Agents](https://mlsanity.com/paper/2505.24878/) (2025)
- [AI and the Future of Digital Public Squares](https://mlsanity.com/paper/2412.09988/) (2024)
- [COGNITION: From Evaluation to Defense against Multimodal LLM CAPTCHA Solvers](https://mlsanity.com/paper/2512.02318/) (2025)
- [LLM Agents Are the Antidote to Walled Gardens](https://mlsanity.com/paper/2506.23978/) (2025)
- [Aura-CAPTCHA: A Reinforcement Learning and GAN-Enhanced Multi-Modal CAPTCHA System](https://mlsanity.com/paper/2508.14976/) (2025)
- [Next-Gen CAPTCHAs: Leveraging the Cognitive Gap for Scalable and Diverse GUI-Agent Defense](https://mlsanity.com/paper/2602.09012/) (2026)
- [The Impact of Scaling Training Data on Adversarial Robustness](https://mlsanity.com/paper/2509.25927/) (2025)
- [HLL: Can Agents Cross Humanity's Last Line of Verification?](https://mlsanity.com/paper/2606.02449/) (2026)
- [Benchmarking of Different YOLO Models for CAPTCHAs Detection and Classification](https://mlsanity.com/paper/2502.13740/) (2025)
- [Process Matters more than Output for Distinguishing Humans from Machines](https://mlsanity.com/paper/2605.06524/) (2026)
- [CAPTCHAs in the Agentic Era: Solvers That Learn from Every Encounter](https://mlsanity.com/paper/2609.02393/) (2026)
- [CaptchaAgent: Verifiable Visual Reasoning for CAPTCHA Solving](https://mlsanity.com/paper/2610.12866/) (2026)

## Abstract (arXiv)

Our work examines the efficacy of employing advanced machine learning methods to solve captchas from Google's reCAPTCHAv2 system. We evaluate the effectiveness of automated systems in solving captchas by utilizing advanced YOLO models for image segmentation and classification. Our main result is that we can solve 100% of the captchas, while previous work only solved 68-71%. Furthermore, our findings suggest that there is no significant difference in the number of challenges humans and bots must solve to pass the captchas in reCAPTCHAv2. This implies that current AI technologies can exploit advanced image-based captchas. We also look under the hood of reCAPTCHAv2, and find evidence that reCAPTCHAv2 is heavily based on cookie and browser history data when evaluating whether a user is human or not. The code is provided alongside this paper.