SoK: AI-Augmented Binary Reversing

2026-06-17 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Software Development & Engineering · Depth: Expert, extended

Summary

This Systematization of Knowledge (SoK) provides the first comprehensive analysis of AI-augmented binary reversing, a field critical for software understanding, vulnerability discovery, and malware investigation. Analyzing 144 research papers published since 2015, the study organizes the literature into 22 binary reversing domains based on inference tasks. It introduces a unified taxonomy that connects traditional analysis techniques, binary-derived artifacts, representation strategies, learning paradigms, and downstream inference tasks, clarifying the emerging roles of Large Language Models (LLMs) and agentic AI systems. The work offers a holistic view of the field's evolution over the past decade, revealing common structures, persistent technical challenges, and evaluation gaps, while identifying promising future research opportunities for reliable and scalable AI-augmented binary reversing systems.

Key takeaway

For AI Security Engineers developing or deploying AI-augmented binary analysis systems, you must prioritize robust evaluation and diverse corpus construction. Recognize that proxy ground truth and upstream tool dependencies introduce validity risks, and reported gains may reflect hyperparameter tuning. Focus on building systems that integrate multimodal evidence and move towards goal-driven agentic reasoning. Ensure transparency in evaluation setups and address potential distribution shifts for reliable, scalable solutions.

Key insights

AI-augmented binary reversing complements conventional methods, leveraging artifacts as an interface for learning-based semantic inference.

Principles

• AI-augmented reversing complements, rather than replaces, conventional methods.
• Corpus diversity is as critical as corpus scale for semantic generalization.
• Proxy labels and upstream tool errors limit model quality.

Method

The AI-augmented pipeline transforms binary-derived artifacts into model-consumable representations via canonicalization, tokenization, encoding, and embedding, then applies various learning paradigms for semantic inference.

In practice

• Prioritize corpus diversity over mere scale for robust models.
• Deduplicate datasets carefully to prevent train-test leakage.
• Validate AI conclusions with multiple evidence sources.

Topics

• Binary Reversing
• AI-Augmented Analysis
• Large Language Models
• Agentic AI Systems
• Software Security
• Evaluation Practices

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.