CTIFoundry: An Agent-Native Corpus Scaffold for Cyber Threat Intelligence
TL;DR
It is argued that this substrate, not model capability, is the bottleneck on agentic CTI investigation, and CTIFoundry, an agent-native corpus scaffold, is presented.
Abstract
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, 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, 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. The ablation distills design principles for matching corpus scaffolding to data modality, in CTI and beyond. Build-time validation guarantees zero fabricated identifiers by construction, and the scaffold sustains 1,168 investigations end-to-end at about 2.6 cents each.
