Two new research papers propose methods for benchmarking and recovering source code from binary functions using large language models (LLMs). The first paper, REFORGE, introduces a pipeline to construct reliable ground truth for LLM evaluation in binary analysis, highlighting the challenges of compiler optimization and alignment uncertainty. The second paper presents a practical pipeline that combines reverse engineering, anchor-based source code retrieval, and LLM reasoning to identify source functions from a database, rather than generating pseudocode. Both approaches aim to improve the accuracy and reliability of LLM applications in reverse engineering and software security. AI
IMPACT Improves LLM capabilities in software security and reverse engineering by providing better evaluation and recovery methods.
RANK_REASON Two academic papers introducing new methods for LLM application in software reverse engineering.
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