InfoQ’s new overview of model poisoning breaks down how attackers can manipulate training data or learning dynamics to compromise machine-learning systems.
The article covers techniques including label flipping, backdoors, clean-label poisoning, and gradient manipulation, with attention to why poisoned behavior can be difficult to detect.
For AI teams, the takeaway is operational: model security has to include dataset provenance, anomaly detection, evaluation, and monitoring, not just endpoint protection after deployment.