Episode 256: Recursive Pollution? Data Feudalism? Gary McGraw On LLM Insecurity

### Executive summary: This Security Ledger podcast transcript features Gary McGraw (Berryville Institute of Machine Learning) summarizing BIML's report on architectural risks of large language models. It highlights key issues — opaque foundation models, dataset assembly and poisoning, recursive pollution from AI-generated training data, hallucinations/untrustworthy outputs, data feudalism, reproducibility/economic barriers — and urges transparency of dataset provenance, targeted regulation of foundation models, enterprise risk assessment, and the creation of machine-learning security governance.