Open and completed research requests

Modern LLM coding assistants have evolved into tool-using agents that can read repositories, execute commands, modify infrastructure, and trigger external side effects, expanding the attack surface far beyond classic prompt misuse. This project will produce a deep technical survey that systematizes prompt-injection and related agentic risks across runtime execution and offline codebase design, with a focus on coding, CI/CD, MCP, RAG, and plugin ecosystems. The survey will pair threat taxonomy with defense taxonomy, clearly separating prevention, detection, containment, observability, and recovery controls, and distinguishing model-level mitigations from mandatory architectural controls. It will deliver practical secure workflows, configuration patterns, red-team suites, and a maturity model for teams moving from basic assistants to production-grade semi-autonomous engineering agents. The expected impact is a concrete, implementation-oriented reference that helps organizations deploy AI-assisted development safely without relying on prompt-only security assumptions.