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Polar: LLM-Powered Synthesis of Real-World Cyber Evidence for Prioritization and Mitigation
Preprint   Open access

Polar: LLM-Powered Synthesis of Real-World Cyber Evidence for Prioritization and Mitigation

Luoxi Tang, Yuqiao Meng, Ankita Patra, Weicheng Ma, Muchao Ye and Zhaohan Xi
arXiv
arXiv
10/05/2026
DOI: 10.48550/arxiv.2610.07298
url
https://doi.org/10.48550/arxiv.2610.07298View
Preprint (Author's original) This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Cyber threat analysis increasingly depends on evidence distributed across vendor advisories, vulnerability databases, and threat intelligence sources. Turning these fragmented observations into timely decisions requires models to connect technical severity with evolving exploitation evidence and available defensive actions. We present POLAR, an LLM-powered framework for synthesizing real-world cyber evidence into threat-centric assessments for prioritization and mitigation. POLAR first disentangles overlapping incidents and grounds each threat in source-linked evidence. For prioritization, it infers severity metrics from cyber evidence and combines the resulting assessment with temporally ordered exploitation signals to estimate near-term exploitation likelihood. For mitigation, it links the synthesized threat data to authoritative remediation knowledge and organizes applicable actions according to threat urgency and operational constraints. We evaluate POLAR on real-world vulnerability evidence collected from public resources and compare it with multiple baselines. Across heterogeneous incidents and zero-day settings, POLAR improves threat ranking and mitigation retrieval while producing evidence-linked intermediate assessments that support analyst inspection. The results establish evidence synthesis as a practical foundation for LLM-based cyber decision support across related security tasks.
 Computer Science - Artificial Intelligence Computer Science - Cryptography and Security

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