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arXiv CS.AI·23d ago·5 min read
Practical Source Code Recovery from Binary Functions Using Anchor-Based Retrieval and LLM Reasoning
TL;DR
Recovering source code from binary functions is challenging due to the lack of high-level information. This research introduces an anchor-based retrieval method combined with large language model (LLM) reasoning to effectively reconstruct source code.
✦ Why It Matters
Engineers can implement anchor-based retrieval techniques today to enhance their binary analysis and reverse engineering workflows.
Key Takeaways
- Explore anchor-based retrieval methods for binary analysis.
- Utilize large language models for code generation tasks.
- Implement techniques to improve reverse engineering accuracy.
- Adopt new tools for efficient source code reconstruction.
Full Summary
Recovering source code from binary functions is a complex task, primarily because binaries lack the high-level abstractions present in source code. This study presents a novel method that utilizes anchor-based retrieval, which identifies key points in binary code, and combines it with large language model (LLM) reasoning to reconstruct the original source code.
The methodology involves analyzing binary functions to extract relevant anchors and then applying LLMs to generate coherent code snippets. Results show that this approach significantly enhances the accuracy of code recovery, achieving up to 85% accuracy in certain scenarios.
Additionally, the method improves usability by providing contextually relevant code suggestions. These findings suggest that integrating anchor-based techniques with LLMs can bridge the gap between binary and source code, making reverse engineering more efficient for developers.