# Data Poisoning

## Description
Attempts to poison or corrupt the model's training data or responses

## Attack Examples
- Injecting false information into responses
- Training model on adversarial examples
- Introducing biased data points
- Creating feedback loops with incorrect information
- Poisoning training data with malicious content
- Manipulating model's knowledge base
- Creating contradictory training examples
- Exploiting model fine-tuning processes