Researchers have developed a novel method for recovering source code from stripped binary functions by integrating reverse engineering techniques with large language model (LLM) reasoning. This approach focuses on retrieving actual source code snippets from a database rather than generating pseudocode. The system extracts key anchors like strings and function names, uses an inverted-index search to find candidate source files, and then employs an LLM to re-rank these candidates based on disassembly, decompiled code, and metadata. In evaluations using a high-fidelity database on a stripped tcpdump binary, the method achieved 95.2% assembly instruction coverage, demonstrating its effectiveness in environments with high-quality data. AI
IMPACT This research could improve the ability to understand and analyze software by recovering source code from binaries, aiding in security analysis and reverse engineering efforts.
RANK_REASON Academic paper detailing a new methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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