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TITAN: Graph-Executable Reasoning for Cyber Threat Intelligence

arXiv 2510.14670 ↗First posted 16 Oct 2025cs.AI cs.CL cs.CRcited by 0

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Abstract (arXiv)

TITAN (Threat Intelligence Through Automated Navigation) is a framework that connects natural-language cyber threat queries with executable reasoning over a structured knowledge graph. It integrates a path planner model, which predicts logical relation chains from text, and a graph executor that traverses the TITAN Ontology to retrieve factual answers and supporting evidence. Unlike traditional retrieval systems, TITAN operates on a typed, bidirectional graph derived from MITRE, allowing reasoning to move clearly and reversibly between threats, behaviors, and defenses. To support training and evaluation, we introduce the TITAN Dataset, a corpus of 88209 examples (Train: 74258; Test: 13951) pairing natural language questions with executable reasoning paths and step by step Chain of Thought explanations. Empirical evaluations show that TITAN enables models to generate syntactically valid and semantically coherent reasoning paths that can be deterministically executed on the underlying graph.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 20 references that are in the map. First posted on arXiv 16 Oct 2025.

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References · 8

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  3. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  4. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 328
  5. 2024Knowledge Graph-Enhanced Large Language Models via Path Selectioncited by 28
  6. 2025Reasoning of Large Language Models over Knowledge Graphs with Super-Relationscited by 21
  7. 2024Actionable Cyber Threat Intelligence using Knowledge Graphs and Large Language Modelscited by 14
  8. 2024MoRSE: Bridging the Gap in Cybersecurity Expertise with Retrieval Augmented Generationcited by 8

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  1. 2026Beyond RAG for Cyber Threat Intelligence: A Systematic Evaluation of Graph-Based and Agentic Retrieval
  2. 2026NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturing
  3. 2026Reasoning Topology Matters: A Controlled Study of LLM-Based Cybersecurity Analysis
  4. 2023On the Security Risks of Knowledge Graph Reasoning
  5. 2025AttackSeqBench: Benchmarking the Capabilities of LLMs for Attack Sequences Understanding
  6. 2024Crimson: Empowering Strategic Reasoning in Cybersecurity through Large Language Models
  7. 2025ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat Investigation
  8. 2025Reasoning Under Threat: Symbolic and Neural Techniques for Cybersecurity Verification