Adversarial Configuration Injection in AI Coding Assistants: A Systematic Security Evaluation Framework

As AI coding assistants gain widespread adoption in software development workflows, they increasingly receive access to system resources and sensitive data, creating novel attack surfaces for malicious actors. This paper presents the first comprehensive security evaluation framework for detecting prompt injection vulnerabilities in AI agents, specifically focusing on command substitution attacks through environment configuration files.

I developed a systematic testing methodology and multi-layered detection architecture that combines network traffic forensics, behavioral pattern analysis, and file integrity monitoring. The framework encompasses 137 test scenario specifications across five attack categories. Through validation testing, the detection mechanisms successfully identified command execution attempts with 95.6% accuracy and data exfiltration patterns with high precision across diverse attack scenarios.

The framework introduces novel approaches for evaluating non-deterministic AI behavior, including probabilistic vulnerability assessment and multi-turn conversation security analysis. This work addresses critical security gaps as autonomous AI systems deploy at enterprise scale, providing practical guidance for safe AI agent deployment.

Key contributions include: (1) first systematic framework for configuration-based prompt injection testing, (2) multi-dimensional detection architecture applicable to various AI systems, (3) ethical testing methodology for AI security research, and (4) open-source implementation enabling reproducible evaluation.