SecureBox: A Layered Control Protocol for Safe AI-Assisted Coding

With the proliferation of the capabilities and applicability of generative AI, the development of control protocols to combat systemic safety compromise is not trivial. Existing defenses like prompt-level safety instructions, single model oversight, and static policies are susceptible to workarounds, and fail to catch subtle contextual threats such as unsafe logic, stealthy backdoor insertion, and multi-step threat accumulation under the guise of intelligent prompting. In this project, we develop a security-focused AI coding pipeline designed to mitigate these issues through stratified control mechanisms. Our system combines a prompt refining module that structures and sanitizes user inputs, an advanced security agent that analyzes generated code and commands for potentially harmful behavior, and a benchmarking framework to evaluate the overall pipeline in comparison to popular models. We implement multiple protocols—including baseline, reprompting-only, security-only, and combined approaches—and test them on a curated dataset of both benign and adversarial tasks. Our results show that integrating prompt-level defenses with downstream security analysis can significantly reduce synthesis of unsafe outputs while maintaining strong task performance (e.g., X% reduction in unsafe actions with minimal drop in success rate). Overall, this work demonstrates the value of modular, defense-in-depth designs and provides a reproducible framework for evaluating safety–usefulness tradeoffs in AI-assisted software development.