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.