Context-Guided Decompilation: A Step Towards Re-executability
This addresses a critical issue in software security and reverse engineering by enhancing practical reliability, though it is incremental as it builds on existing neural approaches.
The paper tackled the problem of binary decompilation often failing to produce re-executable source code, especially for optimized binaries, by proposing ICL4Decomp, a hybrid framework using in-context learning with LLMs, which achieved around 40% improvement in re-executability over state-of-the-art methods.
Binary decompilation plays an important role in software security analysis, reverse engineering, and malware understanding when source code is unavailable. However, existing decompilation techniques often fail to produce source code that can be successfully recompiled and re-executed, particularly for optimized binaries. Recent advances in large language models (LLMs) have enabled neural approaches to decompilation, but the generated code is typically only semantically plausible rather than truly executable, limiting their practical reliability. These shortcomings arise from compiler optimizations and the loss of semantic cues in compiled code, which LLMs struggle to recover without contextual guidance. To address this challenge, we propose ICL4Decomp, a hybrid decompilation framework that leverages in-context learning (ICL) to guide LLMs toward generating re-executable source code. We evaluate our method across multiple datasets, optimization levels, and compilers, demonstrating around 40\% improvement in re-executability over state-of-the-art decompilation methods while maintaining robustness.