mlsanityData to 14 Oct 2026

Adapting Large Language Models to Emerging Cybersecurity using Retrieval Augmented Generation

arXiv 2510.27080 ↗First posted 31 Oct 2025cs.CR cs.AIcited by 2

On the map: Retrieval-augmented generation → Privacy and security of RAG

Abstract (arXiv)

Security applications are increasingly relying on large language models (LLMs) for cyber threat detection; however, their opaque reasoning often limits trust, particularly in decisions that require domain-specific cybersecurity knowledge. Because security threats evolve rapidly, LLMs must not only recall historical incidents but also adapt to emerging vulnerabilities and attack patterns. Retrieval-Augmented Generation (RAG) has demonstrated effectiveness in general LLM applications, but its potential for cybersecurity remains underexplored. In this work, we introduce a RAG-based framework designed to contextualize cybersecurity data and enhance LLM accuracy in knowledge retention and temporal reasoning. Using external datasets and the Llama-3-8B-Instruct model, we evaluate baseline RAG, an optimized hybrid retrieval approach, and conduct a comparative analysis across multiple performance metrics. Our findings highlight the promise of hybrid retrieval in strengthening the adaptability and reliability of LLMs for cybersecurity tasks.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation. Builds on 23 of its 38 references that are in the map. First posted on arXiv 31 Oct 2025.

Builds on

23 of 38 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation for Evaluating LLMs in Cybersecurity Knowledge12 Feb

Other references · 22

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  4. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 452
  5. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 418
  6. 2024The Faiss librarycited by 376
  7. 2024Seven Failure Points When Engineering a Retrieval Augmented Generation Systemcited by 139
  8. 2022Text and Patterns: For Effective Chain of Thought, It Takes Two to Tangocited by 128
  9. 2024CyberSecEval 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large Language Modelscited by 89
  10. 2024Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoningcited by 88
  11. 2024Large Language Models for Cyber Security: A Systematic Literature Reviewcited by 68
  12. 2024CTIBench: A Benchmark for Evaluating LLMs in Cyber Threat Intelligencecited by 62
10 more
  1. 2024CYBERSECEVAL 3: Advancing the Evaluation of Cybersecurity Risks and Capabilities in Large Language Modelscited by 58
  2. 2024Improving Retrieval for RAG based Question Answering Models on Financial Documentscited by 49
  3. 2024Understanding the Performance and Estimating the Cost of LLM Fine-Tuningcited by 48
  4. 2024Large Language Models in Cybersecurity: State-of-the-Artcited by 34
  5. 2024SEvenLLM: Benchmarking, Eliciting, and Enhancing Abilities of Large Language Models in Cyber Threat Intelligencecited by 22
  6. 2024SecBench: A Comprehensive Multi-Dimensional Benchmarking Dataset for LLMs in Cybersecuritycited by 21
  7. 2024SECURE: Benchmarking Large Language Models for Cybersecuritycited by 14
  8. 2024Actionable Cyber Threat Intelligence using Knowledge Graphs and Large Language Modelscited by 14
  9. 2024A RAG-Based Question-Answering Solution for Cyber-Attack Investigation and Attributioncited by 9
  10. 2024LLMSecCode: Evaluating Large Language Models for Secure Codingcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026LLMs for Zero-Shot Threat Detection via Structured Risk Indicatorscited by 0
  2. 2026NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturingcited by 0

Similar, not cited

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  5. 2025Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps
  6. 2025CTIConnect: A Benchmark for Retrieval-Augmented LLMs over Heterogeneous Cyber Threat Intelligence
  7. 2024Cyber Knowledge Completion Using Large Language Models
  8. 2025RAG Security and Privacy: Formalizing the Threat Model and Attack Surface