Analysis
AI supply chain attacks: Definitions, defenses, and best practices
Publication date unknown · Discovered chainguard.dev
Page published
Publication date unknown · First observed: 22 Oct 2026
Coverage timeline
Single-source analysis — one report is available.
Why it matters
AI supply-chain compromises can introduce hidden malicious behavior through models, dependencies, and plugins that defenders intentionally trust and deploy.
Chainguard's guide explains AI supply-chain attacks against trusted upstream components, including model weights, training data, dependencies, container images, CI/CD pipelines, and agent plugins. The article describes how poisoned artifacts can distribute compromise across downstream AI applications, rather than presenting a newly demonstrated exploit.