 This poses a significant risk to the operational safety and security of applications.  These risks show the importance of a rigorous review processes, with Oversigh Continuous validation mechanism Disclaimers on risk Common Examples of Vulnerability LLM provides inaccurate information as a response, causing misinformation LLM produces logically incoherent or nonsensical text that, while grammatically correct, doesn't make sense LLM melds information from varied sources, creating misleading content LLM suggests insecure or faulty code, leading to vulnerabilities when incorporated into a software system Failure of provider to appropriately communicate the inherent risks to end users, leading to potential harmful consequences.  How to Prevent Regularly monitor and review the LLM outputs.  Use self-consistency or voting techniques to filter out inconsistent text.  Comparing multiple model responses for a single prompt can better judge the quality and consistency of output Cross-check the LLM output with trusted external sources