0 Example Attack Scenarios The LLM generative AI prompt output can mislead users of the application which can lead to biased opinions, followings or even worse, hate crimes etc If the training data is not correctly filtered and|or sanitized, a malicious user of the application may try to influence and inject toxic data into the model for it to adapt to the biased and false data A malicious actor or competitor intentionally creates inaccurate or malicious documents which are targeted at a model's training data in which is training the model at the same time based on inputs.  The victim model trains using this falsified information which is reflected in outputs of generative AI prompts to it's consumers The vulnerability Prompt Injection could be an attack vector to this vulnerability if insufficient sanitization and filtering is performed when clients of the LLM application input is used to train the model.  I. E, if malicious or falsified data is input to the model from a client as part of a prompt injection technique, this could inherently be portrayed into the model data.  How to Prevent Verify the supply chain of the training data, especially when sourced externally as well as maintaining attestations, similar to the "SBOM" (Software Bill of Materials) methodology Verify the correct legitimacy of targeted data sources and data contained obtained during both the training and fine-tuning stages Verify your use-case for the LLM and the application it will integrate to