CTIFoundry: An Agent-Native Corpus Scaffold for Cyber Threat Intelligence

Cyber threat intelligence (CTI) is increasingly consumed not by human analysts but by LLM agents that compose multi-step investigations at query time. The harness side of this shift has matured rapidly (planning loops, tool protocols, context management), but the corpus side has not: threat reports and vulnerability databases are still packaged for retrieval-augmented generation, as opaque chunks behind an embedding index. We argue that this substrate, not model capability, is the bottleneck on agentic CTI investigation, and present CTIFoundry, an agent-native corpus scaffold. At build time, CTIFoundry materializes the latent structure of a CTI corpus: a deterministic ontology graph over four authoritative knowledge bases (CVE, CWE, CAPEC, ATT&CK) whose official cross-references become typed, traversable edges; a span-grounded report layer whose canonical, alias-resolved cross-vendor entities index provenance-carrying chunks; and hybrid dense+lexical retrieval surfaces. At query time this structure is exposed through seven typed tools and three procedural skills mounted on a stock, widely-used open-source agent harness. On the public CTIConnect benchmark (nine tasks over entity linking, attribution, and multi-document synthesis), swapping only the action surface lifts the identically-harnessed agent from 0.610 to 0.829 overall F1 with gpt-5.4 and from 0.470 to 0.745 with claude-haiku-4-5: a small model on CTIFoundry surpasses a flagship model on the flat substrate. The scaffolded agent is simultaneously more accurate and more efficient: on both Claude models it answers with roughly half the tool calls per question. Together these results give design principles for corpus scaffolding over data of different modalities, in CTI and beyond.