Large language models (LLMs) are being incorporated into systems that require adherence to dynamic, machine-interpretable interfaces, prompting an evaluation of their ability to interpret context-free grammars. Researchers assessed LLMs' capacity to generate outputs that are syntactically valid, behaviorally functional, and semantically faithful when given a novel grammar. The introduction of the RoboGrid framework enabled the disentanglement of syntax, behavior, and semantics, facilitating a more nuanced understanding of LLMs' interpretive capabilities1. This research has significant implications for the security of systems that rely on LLMs, particularly in the context of decentralized finance (DeFi) developments, which are redefining both the capabilities and risk profiles of these models. As LLMs continue to evolve, their security implications will likely trail the hype surrounding their development, making it essential for practitioners to prioritize a thorough understanding of their interpretive limitations.