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technologyreview.com·2h ago
AI Research
arXiv CS.AI·23d ago·5 min read
REFORGE: A Method for Benchmarking LLMs' Reverse Engineering Capabilities in Decompiled Binary Function Naming
TL;DR
Reverse engineering decompiled binary functions is challenging, particularly in naming them accurately. REFORGE is a benchmarking method developed to evaluate large language models (LLMs) in this task.
✦ Why It Matters
Engineers can use REFORGE to evaluate and select the most effective LLM for their reverse engineering tasks.
Key Takeaways
- Implement REFORGE to benchmark LLMs for function naming tasks.
- Analyze LLM performance variations to select the best model.
- Refine training datasets for improved accuracy in function naming.
- Explore automated tools for enhancing software analysis and security.
Full Summary
Reverse engineering involves analyzing software to understand its components and functionality, often requiring accurate naming of decompiled binary functions. REFORGE was created as a benchmarking method to assess the capabilities of large language models (LLMs) in this specific area.
The methodology includes generating a dataset of binary functions and their corresponding names, which LLMs are then tasked with predicting. Results showed that while some models performed well, others struggled, with accuracy rates varying significantly across different LLMs.
For instance, the top-performing model achieved an accuracy of 75%, while others fell below 50%. These findings suggest that while LLMs can assist in reverse engineering, there is still a need for refinement in their training and application.
This research opens avenues for improving automated tools in software analysis and security.