Key Security and Trust Risks to Know When Building LLM-Based Systems

Large language models used in production environments carry several technical risks that developers and users often overlook. Hallucination is a primary concern, where a model generates confident-sounding responses that lack factual grounding, potentially leading to wrong decisions. Retrieval-Augmented Generation (RAG) systems introduce additional vulnerabilities, including data poisoning and prompt injection, where malicious content in external sources can manipulate model behavior. As LLMs gain tool-use and function-calling capabilities, they can take real-world actions without explicit user approval, reducing human oversight. Understanding these red flags is critical for anyone building or relying on AI systems in high-stakes environments.