Quantifying and Mitigating Korean Jamo-Level Typographical Vulnerabilities in Large Language Models

• URL: http://arxiv.org/abs/2608.30229v1
• Date: Mon, 31 Aug 2026 04:28:14 GMT
• Title: Quantifying and Mitigating Korean Jamo-Level Typographical Vulnerabilities in Large Language Models
• Abstract summary: Korean introduces an additional typographical perturbation level not captured by ordinary character-level edit models.<n>Because syllable blocks are internally composed of sub-character units called jamo, keyboard-level errors can occur within a syllable, either producing a valid but semantically altered character or exposing raw jamo on the surface.<n>To quantify this vulnerability, we apply five jamo-level perturbation types to the KMMLU benchmark and evaluate four language models.<n>We propose Typo-Aware Chain-of-Thought (TACoT), which routes inputs to chain-of-thought inference only when the probe detects a likely typo
• Score: 12.893256474784499
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Korean introduces an additional typographical perturbation level not captured by ordinary character-level edit models: because syllable blocks are internally composed of sub-character units called jamo, keyboard-level errors can occur within a syllable, either producing a valid but semantically altered character or exposing raw jamo on the surface. Both outcomes disrupt sub-word tokenization and are not reliably corrected by existing grammatical error correction pipelines, leaving LLMs directly exposed to corrupted inputs. To quantify this vulnerability, we apply five jamo-level perturbation types to the KMMLU benchmark and evaluate four language models, finding that accuracy declines monotonically with perturbation intensity and that parameter scaling does not confer robustness against intra-syllabic noise. We further show that typo-corrupted inputs induce a distinct shift in internal representations that is not reducible to ordinary answer incorrectness, and that a simple linear probe trained on these representations detects unseen perturbation types with high AUROC. Motivated by this signal, we propose Typo-Aware Chain-of-Thought (TACoT), which routes inputs to chain-of-thought inference only when the probe detects a likely typo, recovering a substantial portion of the CoT accuracy gain at a fraction of the inference cost.

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