This article explores the use of AI models, particularly Claude Opus 4.6, to detect hidden backdoors in binary executables. While some success was noted, with a 49% detection rate for obvious backdoors, the approach remains unreliable for production use due to high false positives and limitations in analyzing complex binaries.

OGhidra integrates Large Language Models with Ghidra for AI-assisted binary analysis. Users can query Ghidra using natural language, automate reverse engineering tasks, and analyze binaries more efficiently. It supports local AI models for privacy and offers tools for malware analysis and vulnerability research.