arXiv:2606.02834v1 Announce Type: cross Abstract: 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 classi
The increasing sophistication of cyber threats and the limitations of current malware analysis push the need for more advanced, AI-driven solutions.
This development allows AI to directly analyze byte code, circumventing expensive and error-prone lifting tools, leading to potentially faster and more accurate threat detection.
Traditional software vulnerability and malware analysis methods will be supplemented or potentially replaced by AI models capable of native byte interpretation.
- · Cybersecurity companies
- · AI/ML developers
- · National security agencies
- · Traditional reverse engineering tools
- · Malware authors dependent on obfuscation
Enhanced automated malware detection and analysis capabilities.
A significant reduction in the time and resources needed for identifying new vulnerabilities and exploits.
Potential for AI-driven 'offensive' cyber capabilities to develop and deploy exploits more rapidly, leading to an arms race in cyber warfare.
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Read at arXiv cs.AI