# KGV: Integrating Large Language Models with Knowledge Graphs for Cyber Threat Intelligence Credibility Assessment

arXiv: https://arxiv.org/abs/2408.08088 · HTML: https://mlsanity.com/paper/2408.08088/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2024.

## Built on by (most cited first, 9 of 9)

- [False Alarms, Real Damage: Adversarial Attacks Using LLM-based Models on Text-based Cyber Threat Intelligence Systems](https://mlsanity.com/paper/2507.06252/) (2025)
- [CyberThreat-Eval: Can Large Language Models Automate Real-World Threat Research?](https://mlsanity.com/paper/2603.09452/) (2026)
- [LRCTI: A Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verification](https://mlsanity.com/paper/2507.11310/) (2025)
- [Chain or tree? Re-evaluating complex reasoning from the perspective of a matrix of thought](https://mlsanity.com/paper/2509.03918/) (2025)
- [Efficient Multi-Hop Question Answering over Knowledge Graphs via LLM Planning and Embedding-Guided Search](https://mlsanity.com/paper/2511.19648/) (2025)
- [Automation-Exploit: A Multi-Agent LLM Framework for Adaptive Offensive Security with Digital Twin-Based Risk-Mitigated Exploitation](https://mlsanity.com/paper/2604.22427/) (2026)
- [PropLLM: Propagation-Aware Scene Reconstruction for Network Fault Diagnosis](https://mlsanity.com/paper/2606.00582/) (2026)
- [Schema-Aware Planning and Hybrid Knowledge Toolset for Reliable Knowledge Graph Triple Verification](https://mlsanity.com/paper/2604.04190/) (2026)
- [NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturing](https://mlsanity.com/paper/2609.00604/) (2026)

## Abstract (arXiv)

Cyber threat intelligence (CTI) is a crucial tool to prevent sophisticated, organized, and weaponized cyber attacks. However, few studies have focused on the credibility assessment of CTI, and this work still requires manual analysis by cybersecurity experts. In this paper, we propose Knowledge Graph-based Verifier (KGV), the first framework integrating large language models (LLMs) with simple structured knowledge graphs (KGs) for automated CTI credibility assessment. Unlike entity-centric KGs, KGV constructs paragraph-level semantic graphs where nodes represent text segments connected through similarity analysis, which effectively enhances the semantic understanding ability of the model, reduces KG density and greatly improves response speed. Experimental results demonstrate that our KGV outperforms state-of-the-art fact reasoning methods on the CTI-200 dataset, achieving a 5.7% improvement in F1. Additionally, it shows strong scalability on factual QA and fake news detection datasets. Compared to entity-based knowledge graphs (KGs) for equivalent-length texts, our structurally simple KG reduces node quantities by nearly two-thirds while boosting precision by 1.7% and cutting response time by 46.7%. In addition, we have created and publicly released the first CTI credibility assessment dataset, CTI-200. Distinct from CTI identification datasets, CTI-200 refines CTI summaries and key sentences to focus specifically on credibility assessment.