As enterprises rush to deploy internal LLMs, AI copilots, and autonomous agents, most security conversations focus on familiar threats: prompt injection, jailbreaks, model abuse, and data exfiltration. But some security leaders argue a quieter risk deserves far more attention: what happens when the model’s understanding of reality itself becomes corrupted.
This problem is broadly described as AI data poisoning, though experts use different language depending on where the manipulation occurs. Sometimes it refers to maliciously altering training data so a model learns false information. Sometimes it means poisoning retrieval-augmented generation (RAG) pipelines or other contextual layers that ...