Implicit Patterns in LLM-Based Binary Analysis

2026-03-19

AI Summary

This study addresses the lack of interpretability and unclear exploration mechanisms in multi-step reasoning processes employed by large language models (LLMs) for binary vulnerability analysis. Drawing on a dataset comprising 521 binaries and 99,563 reasoning steps, the authors combine qualitative and quantitative methods with temporal behavior modeling to systematically identify and characterize four stable implicit reasoning patterns that spontaneously emerge during LLM inference: early pruning, path-dependent locking, directed backtracking, and knowledge-guided prioritization. These patterns exhibit distinct temporal roles and measurable features, offering a foundational understanding of how LLMs reason through complex program analysis tasks. The findings not only illuminate the internal dynamics of LLM-based reasoning but also provide a basis for designing more reliable and interpretable automated binary analysis systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• LLM-based binary analysis
• implicit patterns
• reasoning traces
• vulnerability analysis
• token-level behavior

Innovation

• implicit patterns
• LLM-based binary analysis
• reasoning traces
• token-level behavior
• multi-pass reasoning