The July 13, 2025 paper " Measuring What Matters: A Framework for Evaluating Safety Risks in Real-World LLM Applications" introduces a practical framework for evaluating safety risks in real-world Large Language Model (LLM) applications, arguing that current methods focusing only on foundation models are inadequate. This framework consists of two main parts: principles for developing customized safety risk taxonomies and practices for evaluating these risks within the application itself, which often includes components like system prompts and guardrails. It emphasizes the need for organizations to contextualize general risks and create taxonomies that are practical and specific to their operational context, as demonstrated by a case study from a government agency. The document then outlines a safety testing pipeline that involves curating meaningful and diverse adversarial prompts, running automated black-box tests, and evaluating model responses, particularly focusing on the use of refusals as a measure of safety. Source: July 13, 2025 Measuring What Matters: A Framework for Evaluating Safety Risks in Real-World LLM Applications https://arxiv.org/pdf/2507.09820