Strengthening Human-Centric Chain-of-Thought Reasoning Integrity in LLMs via a Structured Prompt Framework

2026-04-06

AI Summary

This work addresses the susceptibility of large language models to hallucination, reasoning drift, and insufficient interpretability in safety-critical tasks by proposing the first structured prompt engineering framework tailored for locally deployed scenarios. The framework explicitly guides models to generate reliable and auditable chains of thought through four complementary dimensions: contextual and scope control, evidence anchoring with traceability, structured reasoning with cognitive control, and safety-specific analytical constraints. Empirical evaluations demonstrate that the approach yields up to a 40% improvement in reasoning performance across multiple model families, maintains consistent gains across model scales, and achieves high inter-annotator agreement in human assessments (Cohen’s κ > 0.80), substantially enhancing reasoning completeness, robustness to interference, and practical utility.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Common-Sense Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Chain-of-Thought (CoT) prompting has been used to enhance the reasoning capability of LLMs. However, its reliability in security-sensitive analytical tasks remains insufficiently examined, particularly under structured human evaluation. Alternative approaches, such as model scaling and fine-tuning can be used to help improve performance. These methods are also often costly, computationally intensive, or difficult to audit. In contrast, prompt engineering provides a lightweight, transparent, and controllable mechanism for guiding LLM reasoning. This study proposes a structured prompt engineering framework designed to strengthen CoT reasoning integrity while improving security threat and attack detection reliability in local LLM deployments. The framework includes 16 factors grouped into four core dimensions: (1) Context and Scope Control, (2) Evidence Grounding and Traceability, (3) Reasoning Structure and Cognitive Control, and (4) Security-Specific Analytical Constraints. Rather than optimizing the wording of the prompt heuristically, the framework introduces explicit reasoning controls to mitigate hallucination and prevent reasoning drift, as well as strengthening interpretability in security-sensitive contexts. Using DDoS attack detection in SDN traffic as a case study, multiple model families were evaluated under structured and unstructured prompting conditions. Pareto frontier analysis and ablation experiments demonstrate consistent reasoning improvements (up to 40% in smaller models) and stable accuracy gains across scales. Human evaluation with strong inter-rater agreement (Cohen's k > 0.80) confirms robustness. The results establish structured prompting as an effective and practical approach for reliable and explainable AI-driven cybersecurity analysis.

Problem

• Chain-of-Thought
• reasoning integrity
• security-sensitive tasks
• prompt engineering
• hallucination

Innovation

• structured prompting
• chain-of-thought reasoning
• reasoning integrity
• security-sensitive AI
• prompt engineering