Implicit Patterns in LLM-Based Binary Analysis
This work addresses the challenge of interpreting implicit reasoning in LLM-based binary analysis for security researchers, representing a foundational step rather than an incremental improvement.
The study tackled the problem of understanding how LLM-based agents organize exploration in binary vulnerability analysis by analyzing 521 binaries and 99,563 reasoning steps, identifying four dominant implicit patterns that form a stable, structured system. This provides the first systematic characterization of LLM-driven binary analysis, offering a foundation for more reliable systems.
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 reasoning traces. These token-level implicit patterns serve as an abstraction of LLM reasoning: instead of explicit control-flow or predefined heuristics, exploration is organized through implicit decisions regulating path selection, commitment, and revision. Our analysis shows these patterns form a stable, structured system with distinct temporal roles and measurable characteristics. Our results provide the first systematic characterization of LLM-driven binary analysis and a foundation for more reliable analysis systems.