下一代验证码：利用认知差距实现可扩展且多样化的GUI智能体防御
Next-Gen CAPTCHAs: Leveraging the Cognitive Gap for Scalable and Diverse GUI-Agent Defense
AI总结：
提出Next-Gen CAPTCHAs框架，通过利用人机在交互感知、记忆、决策和行动上的认知差距，设计动态任务以区分生物用户与AI智能体，实现可扩展的防御。
摘要：
GUI智能体的快速演进使传统验证码过时。尽管之前的基准测试如OpenCaptchaWorld为评估多模态智能体建立了基线，但近期推理密集型模型（如Gemini3-Pro-High和GPT-5.2-Xhigh）的进展已有效突破这一安全屏障，在复杂逻辑谜题（如“Bingo”）上达到高达90%的通过率。为此，我们引入Next-Gen CAPTCHAs，一个可扩展的防御框架，旨在保护下一代网络免受高级智能体的攻击。与静态数据集不同，我们的基准测试基于稳健的数据生成流水线，支持大规模且易于扩展的评估，特别是对于后端支持的类型，我们的系统能够生成实际上无限的验证码实例。我们利用人类与智能体在交互感知、记忆、决策和行动上的持久“认知差距”。通过设计需要自适应直觉而非精细规划的动态任务，我们重新建立了生物用户与人工智能体之间的稳健区分，为智能体时代提供了可扩展且多样化的防御机制。
英文摘要：
The rapid evolution of GUI-enabled agents has rendered traditional CAPTCHAs obsolete. While previous benchmarks like OpenCaptchaWorld established a baseline for evaluating multimodal agents, recent advancements in reasoning-heavy models, such as Gemini3-Pro-High and GPT-5.2-Xhigh have effectively collapsed this security barrier, achieving pass rates as high as 90% on complex logic puzzles like "Bingo". In response, we introduce Next-Gen CAPTCHAs, a scalable defense framework designed to secure the next-generation web against the advanced agents. Unlike static datasets, our benchmark is built upon a robust data generation pipeline, allowing for large-scale and easily scalable evaluations, notably, for backend-supported types, our system is capable of generating effectively unbounded CAPTCHA instances. We exploit the persistent human-agent "Cognitive Gap" in interactive perception, memory, decision-making, and action. By engineering dynamic tasks that require adaptive intuition rather than granular planning, we re-establish a robust distinction between biological users and artificial agents, offering a scalable and diverse defense mechanism for the agentic era.
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。
