AI security research project focused on prompt injection risks in LLM-powered applications. The work involved testing how AI systems handle malicious or hidden instructions inside user inputs, documents, webpages, and other untrusted content.

The assessment explored risks such as instruction bypass, guardrail failure, data leakage, unsafe tool use, and AI output manipulation. It demonstrated the importance of testing the full AI application, not only the model, especially when connected to files, APIs, RAG systems, browsers, or autonomous agents.

Skills demonstrated: AI red teaming, LLM security testing, prompt injection analysis, guardrail evaluation, threat modeling, and secure AI deployment.

Tool: Spike