REFORGE: A Method for Benchmarking LLMs' Reverse Engineering Capabilities in Decompiled Binary Function Naming
📰 ArXiv cs.AI
Learn to benchmark LLMs' reverse engineering capabilities in decompiled binary function naming using REFORGE, a method to measure their accuracy and effectiveness
Action Steps
- Apply REFORGE to benchmark LLMs' reverse engineering capabilities
- Use decompiled binary function naming to test LLMs' accuracy
- Configure a dataset for function-level ground truth construction
- Run experiments to evaluate LLMs' performance using REFORGE
- Compare results to existing benchmarks for LLM-assisted binary analysis
Who Needs to Know This
Security researchers and developers working with LLMs for reverse engineering tasks can benefit from this method to evaluate and improve their models' performance
Key Insight
💡 REFORGE provides a method to measure LLMs' accuracy and effectiveness in reverse engineering tasks, specifically in decompiled binary function naming
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🚀 Benchmark LLMs' reverse engineering capabilities with REFORGE! 🤖
Key Takeaways
Learn to benchmark LLMs' reverse engineering capabilities in decompiled binary function naming using REFORGE, a method to measure their accuracy and effectiveness
Full Article
Title: REFORGE: A Method for Benchmarking LLMs' Reverse Engineering Capabilities in Decompiled Binary Function Naming
Abstract:
arXiv:2607.07738v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly applied to reverse-engineering tasks, and recent threat-intelligence reporting shows them operating inside live offensive-security workflows. Claims about their capability, however, outpace our ability to measure it. Existing benchmarks for LLM-assisted binary analysis treat the construction of function-level ground truth as a solved pre-processing step and report accuracy without disclosing how many
Abstract:
arXiv:2607.07738v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly applied to reverse-engineering tasks, and recent threat-intelligence reporting shows them operating inside live offensive-security workflows. Claims about their capability, however, outpace our ability to measure it. Existing benchmarks for LLM-assisted binary analysis treat the construction of function-level ground truth as a solved pre-processing step and report accuracy without disclosing how many
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