Special-Character Adversarial Attacks on Open-Source Language Model

Ephraiem Sarabamoun

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arXiv · 2508.14070

Special-Character Adversarial Attacks on Open-Source Language Model

Ephraiem Sarabamoun

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Abstract

Large language models (LLMs) have achieved remarkable performance across diverse natural language processing tasks, yet their vulnerability to character-level adversarial manipulations presents significant security challenges for real-world deployments. This paper presents a study of different special character attacks including unicode, homoglyph, structural, and textual encoding attacks aimed at bypassing safety mechanisms. We evaluate seven prominent open-source models ranging from 3.8B to 32B parameters on 4,000+ attack attempts. These experiments reveal critical vulnerabilities across all model sizes, exposing failure modes that include successful jailbreaks, incoherent outputs, and unrelated hallucinations.

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Ephraiem Sarabamoun. 2025-08-12. Special-Character Adversarial Attacks on Open-Source Language Model. https://arxiv.org/abs/2508.14070

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