Large language models (LLMs) for code rely on subword tokenizers, such as byte-pair encoding (BPE), learned from mixed natural language text and programming language code but driven by statistics rather than grammar.As a result, semantically identical code snippets can be tokenized differently depending on superficial factors such as whitespace or identifier naming.To measure the impact of this misalignment, we introduce TOKDRIFT, a framework that applies semantic-preserving rewrite rules to create code variants differing only in tokenization.Across nine code LLMs, including large ones with over 30B parameters, even minor formatting changes can cause substantial shifts in model behavior.Layer-wise analysis shows that the issue originates in early embeddings, where subword segmentation fails to capture grammar token boundaries.Our findings identify misaligned tokenization as a hidden obstacle to reliable code understanding and generation, highlighting the need for grammaraware tokenization for future code LLMs.Benchmark Source Task Input PL Output PL # Samples HumanEval-Fix-py HumanEvalPack (Muennighoff et al., 2023) bug fixing Python Python 164 HumanEval-Fix-java Java Java 164 HumanEval-Explain-py code summarization Python Python 164 HumanEval-Explain-java Java Java 164 Avatar-py2java Avatar (Ahmad et al., 2023; Pan et al., 2024) code translation Python Java 244 Avatar-java2py Java Python 246 CodeNet-py2java CodeNet (Puri et al., 2021; Pan et al., 2024) Python Java 200 CodeNet-java2py Java Python 200