Data poisoning is a type of cyber-attack that aims to corrupt the training data of machine learning models. By introducing malicious or misleading data into the dataset, attackers can manipulate the behavior and outcome of these models.

This can lead to inaccurate predictions, flawed decision-making processes, and ultimately compromised system integrity. Understanding and defending against data poisoning attacks is crucial for maintaining the security and effectiveness of AI-driven technologies.

Large Language Models (LLMs) are susceptible to adversarial attacks that can manipulate their outputs. So, the question is how to prevent data poisoning attack? Here are the ways:

• Robust Training: Training models on diverse datasets to make them resilient to adversarial inputs.
• Input Validation: Implementing checks to validate and sanitize inputs before processing.
• Continuous Testing: Regularly testing the model against known attack vectors to identify and fix vulnerabilities.