A Context-Aware Framework for Secure Prompt Synthesis and Injection Prevention in AI-Assisted Software Engineering

Large Language Models (LLMs) are deeply integrated into contemporary softwareengineering pipelines, driving automation across code generation, vulnerability debugging,system documentation, requirement analysis, and automated test suite synthesis. Despitesignificant gains in developer efficiency, these integrations introduce expanded attacksurfaces and operational vulnerabilities. Chief among these risks is prompt injection, anadversarial vector where manipulated inputs hijack model execution, subvert structuralsystem instructions, and force the generation of insecure or malicious payloads.Concurrently, prompt design remains an unstandardized, manual process highly sensitive tominor syntactical variations, leading to inconsistent code quality and operational instability.This paper presents a unified Context-Aware Prompt Synthesis and Injection PreventionFramework designed to secure and optimize AI-assisted software developmentenvironments. By continuously binding repository-level context analysis with automatedsecure prompt generation, real-time input injection detection, and multi-stage outputvalidation, the framework abstracts prompt construction away from manual error whileenforcing strict security boundaries. We evaluate our architectural artifact against standardprompting configurations, few-shot prompting baselines, and static rule-based security filters.The experimental results demonstrate a substantial reduction in vulnerability to adversarialmanipulation, enhanced code generation consistency, and a minimization of manual promptengineering overhead, thereby establishing an audited path toward resilient AI-drivensoftware operations.