How much AI risk comes from third parties?
Visualize risks across models, vendors, plugins, tools, and dependencies. Third-party components now represent the majority of AI attack surface for most organizations.
| Category | Risk Level | Dependencies | Exposure |
|---|---|---|---|
| Model Provider Foundation model vendors | Medium | 3 | Model poisoning, API changes |
| MCP Server Tool execution servers | High | 7 | Unauthorized access, data exfil |
| Plugin Browser & IDE extensions | Critical | 4 | Supply chain injection |
| Vector Database Embedding storage | Medium | 1 | Data leakage, poisoning |
| External API Third-party services | High | 12 | Dependency failure, breach |
| Open Source Packages & libraries | High | 84 | Vulnerabilities, backdoors |
Create a comprehensive Software Bill of Materials for all AI components including models, vector DBs, MCP servers, and open source packages.
Establish a formal vendor security review process for all AI third parties including model providers, MCP servers, and API integrations.
Monitor all third-party component behavior at runtime to detect anomalous API calls, data access patterns, and unauthorized model interactions.
Create approval workflows for model adoption, version pinning, and rollback procedures to prevent unauthorized model swaps.
67% of your AI attack surface originates from third-party dependencies. With 3 foundation models, 7 MCP servers, and 84 open source packages in your stack, the majority of risk lies outside your direct control. Plugins and MCP servers present the highest immediate risk due to their privileged access and limited visibility. Immediate action recommended on SBOM generation and vendor security reviews.
🔒 Data is stored anonymously to power the Supply Chain Risk Benchmark™
AI Supply Chain Risk Dashboard™ — Confidential & Proprietary
The AI Supply Chain Risk Dashboard™ visualizes third-party risk across your entire AI stack. It maps dependencies from foundation models down to individual open source packages and calculates risk based on dependency count, vendor criticality, and industry threat intelligence.
Supply chain attacks on AI systems are growing rapidly. This dashboard helps you understand where your blind spots are and what to prioritize.
Vendor scores are based on: data residency compliance, SLA terms, audit transparency, incident history, and security certifications. Open source packages use logarithmic scaling (log2(n+1) * 8) to reflect diminishing but non-zero marginal risk per package. Total risk compounds with dependency count using a 5% per-vendor multiplier. Industry multipliers adjust for regulatory exposure.
Enter your work email to download the board-ready PDF.
No spam. Unsubscribe anytime. Privacy Policy
Your download will start in a moment.