Implicit Patterns in LLM-Based Binary Analysis
Binary vulnerability analysis is increasingly performed by LLM-based agents in an iterative, multi-pass manner, with the model as the core decision-maker. However, how such systems organize exploration over hundreds of reasoning steps remains poorly understood, due to limited context windows and implicit token-level behaviors. We present the first large-scale, trace-level study showing that multi-pass LLM reasoning gives rise to structured, token-level implicit patterns. Analyzing 521 binaries with 99,563 reasoning steps, we identify four dominant patterns: early pruning, path-dependent lock-in, targeted backtracking, and knowledge-guided prioritization that emerge implicitly from reasoni...
| Authors | Qiang Li, XiangRui Zhang, Haining Wang |
|---|---|
| Primary category | cs.AI |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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