Large Byte Model: Teaching Language Models About Compiled Code
Malware analysis starts with the raw bytes of an executable program, and tools to "lift" these to higher-level representations, such as assembly, are expensive and subject to error. Large Language Models (LLMs) cannot process raw byte representations and answer questions about them. To this end, we present the first byte-native LLM. Based on a vocabulary expansion technique using a bespoke byte tokenizer, such a model is capable of responding to complex questions about malware binaries, with accuracies ranging from 69% for malware family classification to 98% for architecture classification. Our findings indicate that providing domain knowledge during training is essential for this applic...
| Authors | Florian Störtz, Catalin-Andrei Stan, Alexandru Dinu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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