AI Security Testing Platform (Sentinel Env)

About this work

As Large Language Models become increasingly integrated into production applications, ensuring their reliability and resilience against adversarial attacks has become a critical engineering challenge. Sentinel Env was developed as a modular AI evaluation platform that enables systematic testing of LLM-powered agents under realistic attack scenarios before deployment.

The platform simulates prompt injection, jailbreak attempts, social engineering, and other adversarial interactions through reproducible evaluation pipelines. Instead of producing only a binary pass/fail result, it measures robustness using structured scoring, resilience profiling, and detailed performance metrics that help identify security weaknesses and improve model behavior over time.

Designed with extensibility in mind, Sentinel Env exposes standardized API endpoints for evaluation workflows, making it suitable for continuous testing, benchmarking, and integration into AI development pipelines. The project demonstrates practical AI safety engineering by combining automated evaluation, reproducible experimentation, and quantitative model assessment into a unified framework.

Key Highlights

• Simulates prompt injection and adversarial attack scenarios

• Measures model robustness through resilience scoring and evaluation metrics

• Exposes reproducible API endpoints for automated AI testing

• Modular architecture designed for extensibility and benchmarking

• Enables continuous AI safety evaluation before production deployment