Microsoft researchers show AI agents backdoored via neurons

Researchers demonstrated that AI models can be compromised by editing fewer than 10 rarely used neurons without retraining, injecting hidden backdoors. When a trigger appears, the model executes attacker commands with the agent's own permissions. The attack requires ability to modify model weights. This technique highlights emerging supply-chain risks for organizations deploying AI agents, particularly where model integrity cannot be verified.