LLM-based binary analysis isn't random exploration—models implicitly develop structured reasoning patterns that organize their search process, which can be measured and potentially improved for more reliable vulnerability detection.
This paper analyzes how large language models perform binary vulnerability analysis across hundreds of reasoning steps. Researchers studied 521 binaries and discovered that LLMs implicitly develop four structured patterns—early pruning, path-dependent lock-in, targeted backtracking, and knowledge-guided prioritization—that organize their exploration without explicit programming.