 Naturally, external data sources present higher risk as the model creators do not have control of the data or a high level of confidence that the content does not contain bias, falsified information or inappropriate content.  Common Examples of Vulnerability A malicious actor, or a competitor brand intentionally creates inaccurate or malicious documents which are targeted at a model's training data The victim model trains using falsified information which is reflected in outputs of generative AI prompts to it's consumers A model is trained using data which has not been verified by its source, origin or content The model itself when situated within infrastructure has unrestricted access or inadequate sandboxing to gather datasets to be used as training data which has negative influence on outputs of generative AI prompts as well as loss of control from a management perspective.  Whether a developer, client or general consumer of the LLM, it is important to understand the implications of how this vulnerability could reflect risks within your LLM application when interacting with a non-proprietary LLM.  OWASP. org 10 | OWASP Top 10 for LLM v1