Production MLOps Security: From Model Poisoning to Inference Attacks in 2026

Production MLOps Security: Protecting Your ML Pipeline from Model Poisoning to Inference Attacks Your ML pipeline is only as secure as its weakest stage. In 2026, attackers don't just target your application — they target the models, data, and infrastructure that power it. A poisoned model file, a compromised feature store, or a vulnerable inference endpoint can give attackers a foothold deeper than any traditional web vulnerability. This guide covers the end-to-end security of production MLOps pipelines, with concrete defenses you can implement today. The MLOps Attack Surface A typical production ML pipeline has six attack stages: Data Collection → Feature Store → Training → Model Registry → Serving → Monitoring ↓ ↓ ↓ ↓ ↓ ↓ Poisoning Injection Backdoor Supply Chain SSRF/RCE Evasion Each stage has distinct vulnerabilities. Let's walk through them. Stage 1: Data Pipeline Poisoning The Attack An attacker who can influence your training data — even a small percentage — can implant backdoo