Verifiable Cleanliness for LLMs: Data-SBOM, CAP Protocol, and a Poison Stress Test

Small-budget training-data poisoning makes open LLM pipelines a governance problem, not just a technical one. This paper introduces a practical baseline: a machine-readable Data-SBOM (DSBOM), an operational CAP protocol (Cleanliness–Audit–Provenance), and a reproducible pre-deployment Poison Stress Test (PST), plus metrics (TIGI, R-Persist, S-Frag, A-Score) to measure susceptibility and auditability. Together these artifacts form a testable gate and deployment roadmap for enterprises, model hubs, and regulators to raise provable cleanliness and reduce poisoning risk.