Security experts warn that adversaries can deliberately corrupt AI training datasets to compromise model behavior and reliability.

Poisoning attacks involve injecting malicious or biased data into AI training sets to manipulate model outputs

This threat poses significant risks to AI systems used in critical applications like security, healthcare, and autonomous systems

Defenders must implement data validation, source verification, and anomaly detection to protect training pipelines

The accessibility of AI training data and models makes poisoning attacks increasingly feasible for motivated attackers