Summary
Large Language Model (LLM)-assisted coding, where an LLM automatically generates code based on a developer-specified prompt, is already popular and projected to grow, promising to bolster coding productivity while reducing software development time and effort. LLM-generated code can be insecure for a variety of reasons, for example omitting critical security checks, or containing mistakes that adversaries can exploit. When this insecure code eludes scrutiny and makes its way into production systems, our software infrastructure is at risk. This project advances the state of research and practic
