Reasoning Topology Matters: A Controlled Study of LLM-Based Cybersecurity Analysis
Large Language Models (LLMs) are increasingly used in cybersecurity, where accurate analysis often requires multi-step and context-dependent reasoning over complex and heterogeneous data. However, existing prompting approaches typically focus on eliciting reasoning without explicitly considering how intermediate reasoning steps are structurally organized. We introduce Security Reasoning Topology, which models reasoning through three representative structures: Linear, Branching, and Graph. To evaluate their effects, we conduct controlled experiments on three cybersecurity datasets covering MITRE ATT&CK network traffic, cyber threat intelligence (CTI), and CVE vulnerability analysis. We eva...
| Authors | Jiling Zhou, Aisvarya Adeseye, Antti Hakkala et al. |
|---|---|
| Primary category | cs.CR |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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