ReCopilot: Reverse Engineering Copilot in Binary Analysis (2505.16366v1)

Abstract: Binary analysis plays a pivotal role in security domains such as malware detection and vulnerability discovery, yet it remains labor-intensive and heavily reliant on expert knowledge. General-purpose LLMs perform well in programming analysis on source code, while binaryspecific LLMs are underexplored. In this work, we present ReCopilot, an expert LLM designed for binary analysis tasks. ReCopilot integrates binary code knowledge through a meticulously constructed dataset, encompassing continue pretraining (CPT), supervised fine-tuning (SFT), and direct preference optimization (DPO) stages. It leverages variable data flow and call graph to enhance context awareness and employs test-time scaling to improve reasoning capabilities. Evaluations on a comprehensive binary analysis benchmark demonstrate that ReCopilot achieves state-of-the-art performance in tasks such as function name recovery and variable type inference on the decompiled pseudo code, outperforming both existing tools and LLMs by 13%. Our findings highlight the effectiveness of domain-specific training and context enhancement, while also revealing challenges in building super long chain-of-thought. ReCopilot represents a significant step toward automating binary analysis with interpretable and scalable AI assistance in this domain.

Insights into ReCopilot: Reverse Engineering Copilot in Binary Analysis

The paper "ReCopilot: Reverse Engineering Copilot in Binary Analysis" presents a specialized LLM, aimed at transforming binary analysis, a cornerstone in cybersecurity. Traditional approaches to binary analysis, notably the use of tools like IDA Pro and Ghidra, while effective, often falter due to the absence of symbolic information, such as function names and variable types, in stripped binaries. As a remedy to these limitations, ReCopilot introduces a novel application of LLMs, specifically engineered to understand and analyze binary code.

ReCopilot distinguishes itself by integrating expertly curated binary code knowledge, employing a multifaceted training regimen consisting of continue pretraining (CPT), supervised fine-tuning (SFT), and direct preference optimization (DPO). This approach ensures the model imbues detailed domain-specific understanding, thereby improving accuracy and contextual reasoning capabilities.

Strong Numerical Results and Methodological Insights

ReCopilot showcases impressive performance metrics, outperforming existing binary analysis tools and general-purpose LLMs by an average margin of 13% across various critical tasks, including function name recovery and variable type inference. This margin is a testament to the efficacy of its domain-specific training and the tailored methodology of context enhancement through static program analysis.

The authors' methodological innovation includes constructing a dataset with over 60 billion tokens, utilizing a generator-discriminator framework to automatically generate supervised fine-tuning data with chain-of-thought (CoT) reasoning. Moreover, ReCopilot's context enhancement, incorporating static analysis techniques, further enhances its analytic precision, by generating more informed prompts using variable data flow analysis and call graph context.

Implications and Future Directions

Practically, ReCopilot reduces the manual labor involved in binary analysis, enabling security professionals to focus on higher-value tasks. Theoretically, this work demonstrates the potential of specialized LLMs in tackling domain-specific challenges, extending the applicability of LLMs beyond traditional programming tasks.

The approach outlined in the paper could serve as a blueprint for developing models tailored to other niche areas within cybersecurity, such as vulnerability detection beyond binary code or improving deobfuscation techniques. In terms of future work, expanding ReCopilot’s capabilities to encompass handling binaries from diverse programming languages and more complex data representations like disassembly code would dramatically enhance its utility. Additionally, exploring reinforcement learning to stabilize reasoning processes and leveraging agentic capabilities could further augment the model's effectiveness and adaptability.

Conclusion

Overall, ReCopilot represents a significant step toward automating binary analysis with interpretable AI solutions, and it stands as a vital contribution to the security domain. By refining the granularity of training and improving model reasoning through innovative data-driven techniques, ReCopilot not only advances the application of AI in cybersecurity but also lays groundwork for future computational breakthroughs in understanding binary code. The insights from this paper could catalyze new research endeavors aimed at enhancing cybersecurity strategies in the increasingly complex digital landscape.