AI-Driven Zero-Day Simulation: Predictive Offensive Modeling for Emerging Vulnerabilities

Abstract

The accelerating rate of software development and complexity of dependencies has made classical vulnerability discovery inadequate to protect against new, unanticipated threats. This article presents a machine learning–based system for zero-day vulnerability simulation and predictive exploit modeling that facilitates proactive detection of high-risk attack surfaces before disclosure or exploitation. The system combines neural models for vulnerability prediction, code-level graph analysis, and adversarial learning methods to predict probable weaknesses in software ecosystems. At its essence, the system uses predictive modeling of exploit behavior, learned from past vulnerability and exploit patterns, to simulate how novel attack chains could manifest in unseen codebases or settings. The method combines static code embeddings, semantic pattern matching, and reinforcement learning–based exploit generation to build hypothetical exploit paths with high contextual precision. These simulated zero-day attacks are subsequently verified by automated red teaming pipelines, providing a continuous loop of feedback to continuously refine model accuracy as well as offensive realism. Experimental validation on enterprise-scale software repositories shows the framework's ability to predict likely vulnerability classes with high accuracy and to produce exploitable paths before conventional detection techniques. The results form a basis for predictive offensive security, in which artificial intelligence responds not only to identified attacks but foresees and simulates upcoming attack vectors. This article pushes the frontiers of both AI-enabled vulnerability exploration and autonomous offensive emulation and represents a major advance toward proactive, intelligence-based cyber defense preparedness.