English(EN) COGNITION: From Evaluation to Defense against Multimodal LLM CAPTCHA Solvers

新论文发现，多模态大语言模型对验证码安全构成紧迫威胁

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-15 04:00

一项新的研究论文详细介绍了多模态大语言模型（MLLMs）如何有效解决视觉验证码，构成重大的安全风险。该研究评估了七种MLLMs在18种验证码类型上的表现，发现当前模型能够以接近人类的成本和速度解决许多面向识别和低交互的验证码。研究人员提出了防御策略，包括引入细粒度定位和隐式计数，这使得在一种加固的验证码类型上，MLLMs的成功率从95%以上降至0%。论文强调，随着MLLM能力的进步，迫切需要重新设计验证码。 AI

排序理由该集群包含一篇详细介绍新发现和拟议解决方案的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍新发现和拟议解决方案的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Junyu Wang, Changjia Zhu, Yuanbo Zhou, Lingyao Li, Xu He, Mingkui Wei, Junjie Xiong · 2026-06-15 04:00

COGNITION：从评估到防御多模态大语言模型验证码求解器

arXiv:2512.02318v4 Announce Type: replace-cross Abstract: This paper studies how multimodal large language models (MLLMs) undermine the security guarantees of visual CAPTCHA. We identify the attack surface where an adversary can cheaply automate CAPTCHA solving using off-the-shel…

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报道来源 [1]

COGNITION：从评估到防御多模态大语言模型验证码求解器

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