SoK: AI-Augmented Binary Reversing

2026-06-16 · Source: Artificial Intelligence · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

A new Systematization of Knowledge (SoK) paper, "AI-Augmented Binary Reversing," analyzes 144 research papers published since 2015 to provide the first comprehensive overview of this rapidly evolving field. Binary reversing, crucial for software understanding and vulnerability discovery, faces challenges from semantic information loss during compilation. The study organizes the analyzed literature into 22 binary reversing domains based on inference tasks. It introduces a unified taxonomy that integrates conventional and AI-augmented reversing pipelines, detailing traditional analysis techniques, binary-derived artifacts, representation strategies, learning paradigms, and downstream inference tasks. This taxonomy also clarifies the roles of large language models (LLMs) and agentic AI systems. The research offers a holistic view of the field's evolution over the past decade, identifying common structures, persistent technical challenges, evaluation gaps, and future research opportunities for reliable and scalable AI-augmented systems.

Key takeaway

For AI Security Engineers or Research Scientists focused on binary analysis, this SoK provides a critical framework for navigating the fragmented landscape of AI-augmented reversing. You can use its unified taxonomy to understand current approaches, identify gaps, and pinpoint promising research directions involving LLMs and agentic AI. This will help you make informed decisions on tool selection, research focus, and developing more reliable and scalable binary reversing systems.

Key insights

A new SoK unifies AI-augmented binary reversing, mapping 144 papers across 22 domains with a comprehensive taxonomy.

Principles

• Binary reversing is fundamental but challenging.
• AI adoption fragments the reversing field.
• Unified taxonomies clarify complex domains.

Method

The paper systematizes 144 research papers (since 2015) into 22 domains by inference task, developing a unified taxonomy for conventional and AI-augmented reversing pipelines.

In practice

• Use the taxonomy to map reversing tasks.
• Identify LLM roles in binary analysis.
• Explore agentic AI for vulnerability discovery.

Topics

• Binary Reversing
• AI Augmentation
• Large Language Models
• Agentic AI Systems
• Vulnerability Discovery
• Software Understanding
• Taxonomy Development

Best for: AI Scientist, AI Security Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.