Training data poisoning is a serious threat to machine learning models, particularly large language models (LLMs). When malicious actors intentionally corrupt or manipulate training datasets, it can degrade model performance, introduce biases, and lead to incorrect predictions. This vulnerability has severe implications for critical applications that rely on LLMs, such as autonomous systems and AI-driven decision-making processes.

To prevent training data poisoning, it's essential to implement secure data handling practices during the training phase. This includes using robust data validation, monitoring data integrity, and implementing access controls to ensure only authorized personnel can manipulate datasets.

By taking these precautions, we can help maintain the integrity of AI models and ensure they remain reliable and trustworthy.

Source: https://dev.to/pynt/what-is-training-data-poisoning-in-llms-6-ways-to-prevent-it-ibg