What the Eyes See, the LLMs Miss: Exploiting Human Perception for Adversarial Text Attacks
Large language model (LLM)-powered content moderation systems are a critical defense against harmful online content. However, they operate primarily on tokenized text and often overlook visual cues that humans naturally use when interpreting content. We show that this limitation creates a fundamental vulnerability: content readily recognized as harmful by humans can evade automated moderation. To systematically study this problem, we introduce Human-Perceptible Adversarial Attacks (HPAA), which embed harmful expressions into otherwise benign text using visually salient typographic manipulations. HPAA strategically combines features such as spacing, emphasis, and spatial arrangement to pre...
| Authors | Qin Yang, Lu Malloy, Joshua Lee et al. |
|---|---|
| Primary category | cs.CR |
| Published | 8 June 2026 |
| Last updated at source | 19 June 2026 |
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