5 Warning Signs Your Model is Suffering from LLM Data Poisoning

LLM data poisoning involves subtle manipulation of model behavior through corrupted training data. The article identifies five key warning signs including unusual outputs, performance degradation, model drift, stealth backdoors, and inconsistent metadata, noting attackers need only poison as little as 0.1% of training data to create persistent backdoors.