VIPER Strike: Defeating Visual Reasoning CAPTCHAs via Structured Vision-Language Inference

• URL: http://arxiv.org/abs/2601.06461v1
• Date: Sat, 10 Jan 2026 07:01:53 GMT
• Title: VIPER Strike: Defeating Visual Reasoning CAPTCHAs via Structured Vision-Language Inference
• Authors: Minfeng Qi, Dongyang He, Qin Wang, Lefeng Zhang,
• Abstract summary: Visual Reasoning CAPTCHAs (VRCs) combine visual scenes with natural-language queries that demand compositional inference over objects, attributes, and spatial relations.<n>We present ViPer, a unified attack framework that integrates structured multi-object visual perception with adaptive LLM-based reasoning.<n>ViPer achieves up to 93.2% success, approaching human-level performance across multiple benchmarks.
• Score: 4.830055389040475
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual Reasoning CAPTCHAs (VRCs) combine visual scenes with natural-language queries that demand compositional inference over objects, attributes, and spatial relations. They are increasingly deployed as a primary defense against automated bots. Existing solvers fall into two paradigms: vision-centric, which rely on template-specific detectors but fail on novel layouts, and reasoning-centric, which leverage LLMs but struggle with fine-grained visual perception. Both lack the generality needed to handle heterogeneous VRC deployments. We present ViPer, a unified attack framework that integrates structured multi-object visual perception with adaptive LLM-based reasoning. ViPer parses visual layouts, grounds attributes to question semantics, and infers target coordinates within a modular pipeline. Evaluated on six major VRC providers (VTT, Geetest, NetEase, Dingxiang, Shumei, Xiaodun), ViPer achieves up to 93.2% success, approaching human-level performance across multiple benchmarks. Compared to prior solvers, GraphNet (83.2%), Oedipus (65.8%), and the Holistic approach (89.5%), ViPer consistently outperforms all baselines. The framework further maintains robustness across alternative LLM backbones (GPT, Grok, DeepSeek, Kimi), sustaining accuracy above 90%. To anticipate defense, we further introduce Template-Space Randomization (TSR), a lightweight strategy that perturbs linguistic templates without altering task semantics. TSR measurably reduces solver (i.e., attacker) performance. Our proposed design suggests directions for human-solvable but machine-resistant CAPTCHAs.

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