What is Data Poisoning? Types, Impact, & Best Practices
Data poisoning is a type of cyberattack where malicious actors deliberately manipulate or corrupt datasets meant for training machine learning models, especially large language models (LLMs). Tampering parts of a raw data set with an incorrect, often duplicitous one can negatively impact the result in various ways. Fundamentally, it aims to alter how AI models learn information so that the output is flawed.
