Input Contamination: Why AI Agents Need Spam Filters

Unlike humans who dismiss spam reflexively, AI agents earnestly process every message in their inbox—turning spam into contaminated context that can skew the model's reasoning. Nylas's policy-level spam detection uses DNS reputation checks and header anomaly detection at the SMTP layer to stop malicious messages before they reach the model. This infrastructure-level approach is more cost-effective and reliable than training every downstream prompt to be skeptical of suspicious input.