Implicit Patterns in LLM-Based Binary Analysis

TL;DR

This paper uncovers structured implicit token-level patterns in large language model-based binary analysis, revealing how multi-pass reasoning organizes exploration without explicit control-flow, thus laying a foundation for more reliable analysis systems.

Contribution

It provides the first large-scale analysis of implicit token-level patterns in LLM-driven binary analysis, revealing structured reasoning behaviors and organizing principles.

Findings

Identified four dominant implicit patterns: early pruning, lock-in, backtracking, and prioritization.

Analyzed 521 binaries with 99,563 reasoning steps to characterize these patterns.

Demonstrated that these patterns form a stable, structured reasoning system.

Abstract

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.