Potential and Challenges of Large Language Models for Reverse Engineering

summary

The gist

Reverse Engineering (RE) remains a labor-intensive process central to software security, and this paper systematizes the rapidly evolving field of applying Large Language Models (LLMs) to RE by

In short

This work systematically reviews 44 research papers and 18 open-source projects applying Large Language Models (LLMs) to reverse engineering. It proposes a five-dimensional taxonomy—covering objective, target, method, evaluation strategy, and data scale—to provide a unified framework for comparing existing LLM applications in the field.

Key concepts

Objective: This dimension defines the core purpose of an LLM application in reverse engineering. It categorizes research based on what the study aims to achieve, such as improving performance metrics like analysis speed, enhancing code readability through interpretability, or using LLMs for novel discovery of vulnerabilities.
Target Representations: This classifies what the LLM is analyzing at different levels of abstraction. Targets range from raw binary sequences and assembly code to decompiled source code, showing how models are applied across various stages of program analysis.
Methodological Strategies: This describes the 'how' an LLM interacts with reverse engineering tasks. Methods include simple zero/few-shot prompting for rapid prototyping, fine-tuning for high precision like decompilation, and agent-based systems that integrate LLMs with external tools like debuggers.
Data Scale: This dimension measures the volume of data used to train or test the LLM applications. It ranges from minimal proof-of-concept examples to fine-tuning on moderate datasets, up to massive pre-training on billions of tokens for broad representational capacity.

Terminology used across episodes

Reinforcement Learning (RL) — explained in 24 episodes
Retrieval-Augmented Generation (RAG) — explained in 16 episodes
Interpretability — explained in 15 episodes
Sample Complexity — explained in 15 episodes
Federated Learning — explained in 14 episodes
Generalization — explained in 13 episodes

This episode discusses

The paper

Potential and Challenges of Large Language Models for Reverse Engineering · Read on arXiv

School of Information Studies, McGill University

Reverse engineering (RE) is central to cybersecurity, supporting tasks such as decompilation, deobfuscation, and security analysis. However, RE remains labor-intensive and expertise-demanding, as analysts often manually recover high-level semantics from low-level program representations. Recent advances in large language models (LLMs) provide a promising way to address these challenges through program artifact understanding, semantic reasoning, and tool-augmented problem solving. This potential has stimulated interest in LLM-assisted RE, but existing studies remain scattered across different tasks, targets, methodologies, and evaluation practices. Despite these advances, the literature still lacks a comprehensive survey that consolidates progress, systematizes technical choices, and clarifies open challenges and future opportunities. To fill this gap, we present a systematic survey of LLM-assisted RE, covering 48 peer-reviewed and published research articles identified through our search and selection protocol as of July 1, 2026. We develop a faceted taxonomy that organizes prior studies along six dimensions: task objective, analysis target, methodological approach, evaluation protocol, training scale, and data quality. We extract task formulations and experimental settings from existing studies to support comparison, reproducibility, and future research. From this review, we synthesize research gaps, characterize key challenges, and outline future directions toward more reliable, reproducible, and security-relevant applications of LLMs in RE.

Transcript

Introduction to the show: ident: Security Radio. Generated commentary on the latest security and cryptography papers.

Nadia: I'm Nadia, and with me are Elias and Priya, guest researcher.

Elias: Today's paper: "Potential and Challenges of Large Language Models for Reverse Engineering".

Nadia: Reverse Engineering (RE) remains a labor-intensive process central to software security,

Elias: First, who's behind it and why it matters.

Paper summary: Nadia: So, to summarize this paper, "Potential and Challenges of Large Language Models for Reverse Engineering," the main thesis is that while LLMs are being applied to reverse engineering for things like vulnerability discovery and malware analysis, their role compared to previous machine learning is still unclear because some efforts are just adapting existing pipelines with minimal changes while others are exploring broader reasoning abilities.

Elias: That points to a fundamental difference in approach; it suggests we're not seeing one single way LLMs are being leveraged for RE, but rather a spectrum of applications driven by different goals and capabilities.

Priya: The paper makes the claim that there's a significant gap between low-level code and high-level reasoning that LLMs are trying to close, and they stress that this gap is causing fragmentation in how these models are being used across the field.

Nadia: Precisely; the paper highlights this tic gap as a major issue, showing that without some consolidation, we're seeing task-specific models evaluated by totally disparate methods and lacking any common benchmarks to compare their actual performance.

Elias: It’s concerning that this lack of a unified framework hinders cumulative knowledge building, which is something I worry about when thinking about long-term cryptographic analysis or deep security research.

Priya: Furthermore, they note that the divergence in assumptions between open-source implementations and academic studies means there's a real gap between what people are imagining conceptually and what can actually be deployed in a practical setting.

Nadia: The paper essentially argues that to move forward effectively, we need to address this fragmentation by providing a systematic mapping of all existing LLM applications in RE. This mapping allows us to organize the landscape by objective, target, method, evaluation strategy, and data scale.

Elias: That systematic approach is what makes the paper matter; it moves us away from just looking at isolated experiments and gives us a way to compare different approaches on a more level playing field.

Priya: It seems like this mapping effort is crucial because it helps illuminate the different ways these models are being used, which is vital for understanding where they can actually be applied safely and effectively.

Conclusion: Nadia: Looking at the title, "Potential and Challenges of Large Language Models for Reverse Engineering," it tells us that this isn't just a celebration of what LLMs can do, but rather a serious look at both the opportunities and the significant hurdles we face when trying to use these tools in security analysis.

Elias: I agree; the paper by Hu et al. is important because it moves beyond simply listing cool applications and instead focuses on structuring the entire research ecosystem around LLMs in RE so we can actually assess their real impact.

Priya: From a measurement standpoint, the implication is that we need better ways to evaluate these models consistently, since they’ve pointed out that different evaluation methods obscure whether we are seeing genuine improvements or just superficial changes in performance.

Nadia: Exactly; if we can use this proposed five-dimensional taxonomy, it should help us decide which kinds of tasks—like performance improvement versus interpretability—are most viable right now for security teams to actually adopt.

Elias: And from a cryptographic viewpoint, if the authors manage to bring some order here, it might help us better understand the assumptions these models make when handling code representations, which is something we need to scrutinize closely.

Priya: Ultimately, the implication for the wider field is that we need consolidated frameworks so that knowledge can accumulate more predictably and responsibly as people start applying this technology to security-critical tasks.

Nadia: So, in simple terms, this paper provides a map of where LLMs are in reverse engineering right now, helping us see what's working and what's causing confusion about the field.

Elias: It’s a necessary step toward making sure that when we start using these powerful generative tools for security analysis, we have a clear understanding of their limits and their actual potential.

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