Inside the Red-Team Philosophy for Safer AI Chatbots

Summary

At a WooCommerce Meetup, Infobip AI consultant Danijel Blazsetin described why teams should deliberately attack their own large-language-model chatbots before someone else does. Unlike rule-based bots, LLM-powered systems can interpret intent, generate flexible responses, and call APIs, databases, or customer-data services, but their non-deterministic behavior makes failures difficult to predict. Red-team testing therefore goes beyond conventional quality assurance by probing unexpected and adversarial paths, including prompt injection, character substitution, data leakage, hallucinated advice, unauthorized API actions, and possible regulatory violations. Infobip uses layered defenses such as system instructions, cloud-provider content filters, and repeated testing cycles, while working with Croatian startup Splix AI to simulate persistent real-world abuse with agent-based systems. The article emphasizes that a successful red-team exercise should reveal weaknesses rather than claim perfection. Because models, prices, prompts, and regulations change, chatbot security is presented as an ongoing process involving repeated fixes and client communication. The same trust-and-verify mindset applies to e-commerce systems, where integrations, payment services, and customer data can fail in similarly unpredictable ways.