AI Gated Loader: Teaching Code to Decide Before It Acts

This technical article introduces AI-gated shellcode loaders that collect host telemetry, query an LLM for a compact JSON allow/deny decision with confidence, and only execute when policy thresholds are met, defaulting to deny on errors. Using the HALO project as a reference, it covers telemetry design, prompt construction, JSON extraction, decision logging, and operational controls, and highlights use cases for training, environment awareness, and operator control, along with benefits (auditability, adaptability) and risks (misclassification, policy drift, data sensitivity).