Enterprise AI Analysis
Adapting Large Language Models to Emerging Cybersecurity using Retrieval Augmented Generation
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.
Authors: Arnabh Borah, Md Tanvirul Alam, Nidhi Rastogi
Executive Impact: Revolutionizing Cybersecurity with Adaptive AI
Large Language Models (LLMs) are transforming cybersecurity, but their reliance on static training data and opaque reasoning struggles with the rapid evolution of cyber threats and the need for temporal awareness. This research addresses these critical challenges by introducing an innovative Retrieval-Augmented Generation (RAG) framework. By dynamically injecting up-to-date, domain-specific context, our hybrid RAG approach significantly enhances LLM accuracy, adaptability, and transparency in critical cybersecurity tasks like vulnerability detection and threat intelligence. This breakthrough paves the way for more trustworthy and effective AI-driven security operations, ensuring LLMs can keep pace with emerging risks and provide verifiable insights.
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KCV Accuracy Boost
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CWET Accuracy Boost
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Enhanced Contextual Precision
Deep Analysis & Enterprise Applications
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Core Problem & RAG Basics
Hybrid Retrieval Methodology
Performance & Ablation
Strategic Limitations & Future
The Evolving Cybersecurity Landscape
Traditional LLMs struggle with cybersecurity's fast-paced evolution, often providing unreliable responses due to outdated knowledge or misinterpreting domain-specific terminology. The paper highlights that LLMs are prone to misinterpreting text and struggle with reasoning when information is presented chronologically out of order. This leads to a need for continuous adaptation that traditional fine-tuning cannot meet cost-effectively. Retrieval-Augmented Generation (RAG) emerges as a powerful solution, enriching LLM responses with external, up-to-date context without expensive retraining. RAG not only boosts performance but also offers transparency by grounding answers in retrieved sources, crucial for verifiable evidence in security.
Our Novel Hybrid Retrieval Framework
We propose a sophisticated hybrid retrieval framework to overcome the limitations of traditional RAG in cybersecurity. This framework combines three core strategies: Dense Semantic Retrieval uses FAISS to capture semantic similarity, allowing the LLM to understand related passages even without exact keyword matches. Sparse Keyword-Based Retrieval, leveraging BM25, prioritizes exact lexical matches, which is critical for precise identification of security identifiers like CVE numbers. Finally, a Regular Expression Matching filter for CVEs further refines retrieval for specific CVE-IDs, significantly improving precision and ensuring reliable context even with minimal input. These methods are integrated with a weighted scoring system (parameter 'alpha') to balance semantic and lexical relevance, providing robust and domain-tailored context extraction.
Empirical Validation Across CTI Tasks
Our empirical evaluation, using the KCV and CWET datasets with the Llama-3-8B-Instruct model, demonstrates the significant advantages of our hybrid RAG approach. The hybrid model with regex achieved a 72.7% accuracy on KCV (a 13.5% increase over No RAG) and 92.2% on CWET (a 6.8% increase). Ablation studies revealed that lower temperature settings (e.g., 0.01) consistently yielded higher accuracy, indicating that deterministic decoding is crucial for factual precision in cybersecurity. While embedding models showed varied performance, 'mxbai-embed-large-v1' achieved the highest accuracy, highlighting the trade-off between performance and resource consumption. These results underscore the promise of hybrid retrieval in strengthening LLM adaptability and reliability for CTI tasks.
Expanding Horizons for Adaptive AI
While our hybrid model shows promising results, its current evaluation is limited to CVE and CWE contexts. Future work will expand testing to a broader range of security datasets, including threat advisories and malware reports, to assess robustness across diverse data distributions. We plan to extend regex matching to other identifiers like ATT&CK and CAPEC, enhancing retrieval precision further. Crucially, developing more reliable metrics for measuring faithfulness to retrieved context remains an open challenge. Long-term, we aim to evaluate the framework across different LLM families and scales, explore lightweight post-processing, integrate multimodal retrieval (e.g., diagrams), and assess real-world deployment feasibility regarding latency, cost, and reliability.
Enterprise Process Flow: Hybrid RAG for Cybersecurity
File Extraction
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Document Chunking
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Embedding to VectorDB
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Hybrid Retrieval (Sparse & Dense)
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Context-Augmented LLM Inference
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Answer Generation
+13.5%
KCV Accuracy Boost (Hybrid+Regex vs. No RAG)
| Strategy | KCV Accuracy | Key Characteristics |
|---|---|---|
| No RAG | 59.2% |
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| Baseline RAG | 57.6% |
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| Hybrid RAG + Regex | 72.7% |
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Real-World Example: Firefox Vulnerability Misinterpretation
The research highlights a critical instance where the LLM, despite improved retrieval, misclassified a Firefox for iOS vulnerability (CVE-2024-5022). The prompt explicitly stated the vulnerability affected 'Mozilla Firefox for iOS versions less than 126'. The retrieved evidence (Document 1) directly confirmed this: 'This vulnerability affects ... iOS < 12.6'. Yet, the LLM incorrectly output 'F' (False). This error was attributed to formatting noise in the retrieved context, making it difficult to parse, and the presence of the word 'Focus' in the document potentially confusing 'Firefox Focus' with 'Mozilla Firefox'. This case underscores LLMs' high sensitivity to context formatting and the need for retrieval pipelines to not only select correct documents but also present them in a model-friendly format for reliable reasoning.
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Your AI Implementation Roadmap
A structured approach ensures successful integration and maximum ROI. Here’s a typical journey for deploying advanced AI solutions like hybrid RAG within your enterprise.
Phase 1: Discovery & Strategy
Understand current cybersecurity challenges, define clear objectives for AI integration, and assess existing infrastructure. Develop a tailored strategy for RAG implementation.
Phase 2: Data Engineering & Integration
Identify, cleanse, and structure relevant cybersecurity data sources (CVEs, CWEs, threat intelligence). Build robust data pipelines for efficient retrieval and context integration.
Phase 3: Model Selection & Customization
Select the optimal LLM (e.g., Llama-3-8B-Instruct) and embedding models. Implement and fine-tune the hybrid sparse-dense retrieval mechanism, including domain-specific regex patterns.
Phase 4: Deployment & Validation
Deploy the RAG framework into a production environment. Conduct rigorous testing and validation using established benchmarks and real-world cybersecurity scenarios.
Phase 5: Monitoring & Continuous Improvement
Establish continuous monitoring for performance, accuracy, and adaptability. Implement feedback loops for iterative refinement and adaptation to new threats and data distributions.
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