MCA-Bench：用于评估CAPTCHA对基于VLM攻击的鲁棒性的多模态基准
MCA-Bench: A Multimodal Benchmark for Evaluating CAPTCHA Robustness Against VLM-based Attacks
AI总结：
针对现有CAPTCHA缺乏统一多模态安全评估基准的问题，提出MCA-Bench多模态基准套件，基于微调的VLM破解智能体开展跨模态评估，定量分析CAPTCHA漏洞关联因素并提出设计原则。
摘要：
随着自动化攻击技术的快速发展，CAPTCHA（验证码）仍是抵御恶意机器人的关键防御机制。现有CAPTCHA方案涵盖多种模态，包括静态扭曲文本、混淆图像、交互式点击、滑动拼图与逻辑类问题，但目前领域内仍缺乏统一、大规模的多模态基准来严格评估其安全鲁棒性。为填补这一空白，我们提出MCA-Bench，这是一套全面可复现的基准套件，将异构CAPTCHA类型整合到统一评估协议中。我们以共享的视觉语言模型（VLM）为主干，为每类CAPTCHA微调专用破解智能体，实现了一致的跨模态评估。大量实验表明，MCA-Bench可有效刻画不同攻击设置下现代CAPTCHA设计的漏洞谱系，首次定量分析了挑战复杂度、交互深度与模型可解性三者的关联。基于这些发现，我们提出三条可落地的设计原则，明确了关键开放挑战，为系统性CAPTCHA加固、公平基准测试及更广泛的社区协作奠定基础。数据集与代码已公开上线。
英文摘要：
As automated attack techniques rapidly advance, CAPTCHAs remain a critical defense mechanism against malicious bots. However, existing CAPTCHA schemes encompass a diverse range of modalities -- from static distorted text and obfuscated images to interactive clicks, sliding puzzles, and logic-based questions -- yet the community still lacks a unified, large-scale, multimodal benchmark to rigorously evaluate their security robustness. To address this gap, we introduce MCA-Bench, a comprehensive and reproducible benchmarking suite that integrates heterogeneous CAPTCHA types into a single evaluation protocol. Leveraging a shared vision-language model backbone, we fine-tune specialized cracking agents for each CAPTCHA category, enabling consistent, cross-modal assessments. Extensive experiments reveal that MCA-Bench effectively maps the vulnerability spectrum of modern CAPTCHA designs under varied attack settings, and crucially offers the first quantitative analysis of how challenge complexity, interaction depth, and model solvability interrelate. Based on these findings, we propose three actionable design principles and identify key open challenges, laying the groundwork for systematic CAPTCHA hardening, fair benchmarking, and broader community collaboration. Datasets and code are available online.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
