Glitch tokens are anomalous subword units that consistently trigger unexpected, unstable, or unsafe behaviors in large language models (LLMs). Some popular examples of such tokens include “SolidGoldMagikarp”, “PsyNetMessage”, and “petertodd”. Prior work has largely attributed the vulnerability to data sparsity—tokens that occur too infrequently in pretraining data, leading to poorly trained embeddings. However, this framing overlooks the concept of context sparsity. We hypothesize that a distinct class of glitch tokens emerges not due to rarity, but because they occur in narrow or repetitive contextual environments. These tokens can lead to jailbreaking and adversarial attacks on LLMs. We systematically investigate context-sparse glitch tokens across multiple families of open-source models (Gemma, LLaMA, and Mistral). Using semantic clustering, diversity scoring, and sequential n-gram analysis, we characterize tokens with limited contextual variety and evaluate their impact on robustness. Our experiments reveal mixed evidence for context sparsity effects on the HarmBench dataset, when injected at suffix positions in prompts or embedded within chat templates. These findings broaden the understanding of glitch vulnerabilities, showing that they are not simply artifacts of token rarity but reflect both contextual patterns and structural weaknesses in how LLMs process conversations.