For Heads-of · Practitioner
LLM application supply chain vulnerabilities
Vulnerable or unvetted components — base models, adapters, datasets, plugins, deployment platforms — enter an LLM application through its supply chain.
- high
- llm
- supply-chain
- owasp-llm
How it happens
An LLM application depends on third-party base models, fine-tuning datasets, LoRA adapters, plugins, and hosting platforms, any of which can be vulnerable, mislicensed, or maliciously tampered with before they ever reach the application.
Why it matters
A compromised or mislicensed component inherits into every system built on it, and is far harder to detect after the fact than a compromised code dependency.
Mitigating controls
The controls that address this risk, ranked by effectiveness.
Third-party model and vendor due diligence
Pre-procurement and ongoing due-diligence policy for the selection and contractual oversight of third-party AI models and datasets.
Training data provenance checks
Documented verification of the source, licensing, and integrity of any dataset before it is used for training or fine-tuning.
Framework and clause references
| Framework | Clause | Title |
|---|---|---|
| OWASP Top 10 for LLM Applications | LLM04 | Supply Chain |
| MITRE ATLAS | AML.T0010 | AI Supply Chain Compromise |
Related resources
The external sources behind this risk, from the Resources library.