Compositional Security Control for AI-Assisted Coding Workflows: A Threat-Model-Grounded Security–Productivity Frontier

Abstract

AI-assisted coding systems now influence repository edits, dependency selection, and deployment decisions, which creates coupled attack surfaces spanning prompt-channel abuse, supply-chain compromise, and unsafe escalation policies. We present a compositional security framework that unifies trust-boundary enforcement, action-intent validation, fail-closed provenance gating, and uncertainty-conditioned review routing for mixed-trust coding workflows. The manuscript integrates three formally stated components with executable symbolic checks and evaluates them on a reproducible multi-stage benchmark covering chat-agent, IDE-copilot, and CI-bot settings. Across the current benchmark implementation, the full compositional policy reduces attack success relative to alignment-only and advisory-only comparators while preserving usable task utility, and it yields non-dominated frontier behavior in most modality slices. We also identify bounded failure modes, including parser-evasion sensitivity and high-productivity-penalty regimes, to define where conclusions are stable and where additional validation is needed.