CAPE - Context-Aware Prioritization Engine for Vulnerability Scoring and Management

Vulnerability patching is critical for safeguarding an enterprise's software ecosystem from cyber attacks. However, patching requires adequate preparation for potential system downtime and for unforeseen effects of the patched software on a live system. Thus, effective risk mitigation requires prioritization strategies that explicitly account for the operational context of the affected software. While vulnerability prioritization based on Common Vulnerability Scoring System (CVSS), Exploit Prediction Scoring System (EPSS) and CISA Known Exploited Vulnerabilities (KEV) catalog, can help, their differing goals and methodologies often lead to inconsistent, context-unaware decisions resulting in mis-allocated remediation efforts. In this work, we present the Context-Aware Prioritization Engine (CAPE), a vulnerability analysis framework that enriches each vulnerability scanner report of vulnerabilities (CVEs) with deployment-specific evidence. This evidence is used to perform vulnerability reachability, centrality, and exploitability analysis, producing a ranked priority score for the effective distribution of remediation effort. We evaluate CAPE on 30 open-source agentic applications comprising 6,138 scanner-reported CVEs. Our evaluation shows that a significant majority of CVEs are not reachable and exploitable which results in significant disparities between CAPE and context-free baselines - the CVSS, EPSS, and KEV. It also shows that CAPE consistently surfaces confirmed-exploitable CVEs at higher priority than all baselines, demonstrating that context-aware analysis provides substantially more accurate remediation guidance than context-free severity scoring alone.