Secure compilation of AI generated code

This project addresses the risks of AI-generated code, which acts as a stochastic, untrusted compiler that translates natural language intent into often functionally correct but security-agnostic code. This project will study a novel theory of pluggable security patches to harden these AI-generated code against classical vulnerabilities (e.g., memory safety, non-interference) as well as against microarchitectural threats...