From Code to Compromise: A Unified Framework for Application Security Vulnerability Assessment in the Age of AI-Augmented Attacks

The convergence of traditional application vulnerabilities with AI-augmentedattack techniques represents one of the most significant threat escalationsof the current decade. This paper presents a Unified Application SecurityAssessment Framework (UASAF) that integrates static code analysis, dynamicweb application testing, controlled exploit-chain simulation via Boot2Root(B2R) Capture-the-Flag (CTF) environments, and phishing-vector analysis.Drawing on empirical data collected across three purpose-built B2Rassessment machines---ORACLE (medium), PHANTOM (hard), and ECHO(easy)---real-world static analysis of the SentinelSight network monitoringapplication, and a structured phishing simulation spanning 200 simulatedparticipants, we demonstrate quantifiable patterns in how vulnerabilitiescompound across application layers. Our framework introduces afive-dimensional assessment model encompassing code-level weaknesses,configuration drift, credential exposure, social engineering susceptibility,and AI-amplified threat vectors. Results indicate that 94.3% of exploitableattack chains involved at least two compounding vulnerability classes, andthat organizations lacking structured assessment methodologies face a3.7× higher projected risk exposure. The combined multi-tool staticanalysis pipeline detected 75% more unique findings than any single tool.This research provides practitioners and academics with a reproducible,vendor-neutral methodology supported by structured CTF-based validationthat bridges theoretical vulnerability knowledge and real-world exploitationscenarios.