When the prompt becomes the payload: A practical pen-testing guide for GenAI, LLM and RAG applications
Summary
This article discusses the evolving security landscape of Generative AI applications, moving beyond simple prompt manipulation to concerns about unauthorized data access and business actions. It outlines a practical penetration testing approach, emphasizing an architecture-first strategy and mapping data flow to identify exploit chains. The guidance highlights that risks from prompt injection persist across RAG and fine-tuning, making various content sources potential attack vectors.
IFF Assessment
The article describes new attack vectors and methodologies targeting Generative AI applications, which poses a risk to defenders by exposing novel ways systems can be compromised.
Defender Context
As GenAI becomes integrated into business processes, defenders must consider new attack surfaces where prompts and data can be manipulated. Understanding the full attack chain, from user input to downstream actions and data access, is crucial for identifying and mitigating risks. Organizations should focus on securing the entire AI application architecture, not just the language model itself.