ThreatLens: Evidence-Guided Ranking of High-Priority CVEs

Abstract

Security teams must prioritize vulnerabilities before exploitation evidence is complete. Existing signals, such as CVSS, EPSS, advisories, and public exploits, are useful but fragmented and time-sensitive; retrospective rankings can therefore overstate performance by using evidence unavailable at decision time. We present ThreatLens, a simple yet effective and deployment-realistic framework for CVE prioritization. ThreatLens ranks vulnerabilities at each review point using only cutoff-valid evidence and learns from future CISA KEV entries as weak supervision for exploitation relevance. Under forward-in-time, CVE-disjoint evaluation, ThreatLens significantly outperforms CVSS, EPSS, and rule-based evidence-fusion baselines. On the held-out test split, ThreatLens surfaces 80.0% of future KEV CVEs in the top 20, over three times EPSS at the same budget, and reaches 95.9% in the top 50. Early-warning analysis further shows that ThreatLens identifies a substantial fraction of subsequent KEV entries before formal catalog inclusion, supporting timely, evidence-grounded triage.