What Is Training Data Poisoning in LLMs & 6 Ways to Prevent It

What Is Training Data Poisoning in LLMs?

Training data poisoning occurs when malicious actors intentionally alter or corrupt the data used to train machine learning models, particularly large language models (LLMs). This manipulation can degrade model performance, introduce biases, or cause the model to make incorrect predictions.

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By corrupting the training dataset, adversaries aim to influence the model's behavior in targeted or broad contexts. In LLMs, the implications of data poisoning are severe due to their extensive use in critical applications, from autonomous systems to AI-driven decision-making processes. This vulnerability requires secure data handling practices during the training phase to ensure that the integrity of AI models remains intact.

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