HAVE: Host Active Verification Engine for Closing the Contextual Reality Gap in Security Digital Twins

Abstract

Security Digital Twins (SDTs) provide continuously updated virtual replicas of infrastructure for threat simulation, yet they rely on theoretical CVSS scores to assign lateral-movement probabilities -- creating the Contextual Reality Gap: risk is overestimated where unacknowledged mitigations neutralize exploits, and drastically underestimated where logic flaws bypass all memory-safety defenses. We present the Host Active Verification Engine (HAVE), an SDT extension that deploys a safety-constrained host agent to measure the empirical probability of compromise $\hat{p}$ via maximum-likelihood estimation over snapshot-isolated Bernoulli trials. A Wilson interval-width confidence weight $α_w$ propagates $\hat{p}$ into Monte Carlo simulations via a Bayesian blending rule formally related to the Beta-Binomial posterior. Evaluation across four vulnerability classes, three security tiers, and two production binaries shows HAVE reduces $P_{\text{reach}}$ by 38.2% in false-positive scenarios and increases it by 132.4% in false-negative scenarios, with a net +124.1% correction; post-HAVE estimates vary by only $1.12\times$ across calibration exponents $κ$, versus $4.6\times$ for CVSS-only baselines.