Red Teaming LLM-Powered Systems: Breaking Beyond the Model

The adoption of large language models (LLMs) has led to the development of intelligent systems and tools that require robust security testing beyond the model layer, focusing on how these models function within real-world systems. Galtea addresses this need by employing a red teaming engine that evaluates LLM-based systems as complete products, creating adversarial prompts specific to the product's context and simulating various threat types to test safety filters and logic constraints. Their approach includes defining the product's behavioral contract, threat modeling with built-in and custom threat categories, and using transformation strategies to craft more challenging prompts. This comprehensive system-level testing ensures that AI systems maintain their intended purpose and security boundaries, reflecting real misuse scenarios rather than abstract threats. Galtea's red teaming framework highlights the importance of understanding the specific risks and boundaries of each system to effectively test and improve its resilience against potential attacks.