研究人员发布了Kuna实验性反编译器，该项目在美国空军研究实验室（AFRL）和Metalware的支持下由大语言模型开发1。Kuna在C程序控制流结构化任务上的完美结构率达到44.4%，接近业界成熟工具IDA Pro的45.7%1。项目代码几乎完全由LLM编写，通过自主学习和迭代优化实现了包括20多项angr基础功能的重新实现1。
Kuna以NSA开源工具Ghidra的Rust版本为基础进行改进，结合了二进制分析框架angr的管道架构设计1。LLM通过识别Kuna相对于IDA Pro在基础指标上的不足，自主进行了优化1。
Researchers have released Kuna, an experimental decompiler developed primarily by large language models with support from the Air Force Research Laboratory (AFRL) and Metalware.1 The project demonstrates the capability of LLMs to autonomously learn, iterate, and optimize software development tasks.
Kuna achieves a perfect structure rate of 44.4% when analyzing control flow structures in C programs, approaching the performance of the industry-standard IDA Pro at 45.7%.1 The decompiler represents a Rust-based improvement over NSA's Ghidra, incorporating the pipeline architecture of the angr binary analysis framework.1 Nearly all of the project's code was written by the LLM, which successfully reimplemented over 20 foundational angr capabilities.1 Through self-directed learning, the LLM identified and addressed performance gaps relative to IDA Pro, continuously refining Kuna's output across development iterations.1
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