Model Supply Chain Security

As with traditional software, AI models are rarely built from scratch. They often incorporate third-party weights, datasets, scripts, agents, and even plugins. This layered dependency chain creates opportunities for attackers to inject malicious content or vulnerabilities.

Risks in the AI model supply chain include:

• Poisoned training data: Designed to cause biased or adversarial behavior
• Malicious weights: Model files modified to embed backdoors
• Unvetted code: Helper scripts or preprocessing logic that contain vulnerabilities
• Tampered infrastructure: Exploits in serving platforms or endpoints
• License violations: Inclusion of noncompliant datasets or model weights

These risks are difficult to detect with traditional scanners because they often reside in behavior rather than static code. Securing the AI supply chain requires continuous validation, provenance tracking, and behavioral testing.

How PointGuard AI Helps
PointGuard delivers full-spectrum model supply chain security. It scans models, agents, and datasets for hidden threats, tracks asset provenance, and builds dynamic AI Bills of Materials (AI-BOMs). Security teams get continuous visibility and automated enforcement across the AI lifecycle.
Explore: https://www.pointguardai.com/supply-chain

References:

RSA Conference: Securing the LLM Supply Chain

Google: Securing the AI Software Supply Chain