Exploitability Evaluation Through Symbolic Reasoning: A Neuro-Symbolic Framework for Vulnerability Management - Monego, Diego Luan; Coelho Jr., Claudionor N.; Dahab, Ricardo (Anais do Simpósio Brasileiro de Cibersegurança (SBSeg), 2026)
Modern vulnerability management programs face a gap between prioritization and validation: deciding which of thousands of Common Vulnerabilities and Exposures (CVEs) are actually exploitable in a specific environment. Static metrics (CVSS) and probabilistic predictors (EPSS) are insufficient; Breach-and-Attack-Simulation (BAS) tools are expensive and operationally complex. This paper presents a neuro-symbolic multi-agent framework that combines Large Language Models (LLMs) — extracting structured rules from CVE descriptions — with a Prolog engine that performs deterministic, auditable exploitability inference. On four Linux local-privilege-escalation (LPE) CVEs across vulnerable, mitigated, and patched states, the full configuration reaches 100% accuracy versus 61.5% for a single-shot LLM baseline (McNemar p < 10 −4 ) and 92.4% for a no-Prolog ablation, at roughly one quarter of the neural baseline’s token cost. These preliminary results (four Linux LPE CVEs) suggest the approach is a promising direction — rather than a validated general solution — for a lightweight, interpretable validation layer in Continuous Threat Exposure Management (CTEM) programs.