HighSupply Chain◐Demonstrated
Supply-chain compromise (model/fine-tune/library)
Security & RobustnessDescription
Compromised base models, fine-tunes, datasets, or libraries introduce hidden malicious behaviour.
Example scenario
A popular open model on a hub contains a backdoor; the bank adopts it without verification.
Real-world evidence◐Demonstrated
BadNets (2017) and a large body of neural-trojan literature demonstrate trigger-based backdoor activation in computer vision and NLP models under controlled conditions. There is no publicly confirmed production deployment where a backdoor trigger was activated against a real financial AI system.
Primary mitigations
- Model SBOM
- signed/verified models
- vendor attestations
- dependency scanning
- sandboxed evaluation before adoption.
Detection signals
Model integrity verification; dependency vulnerability scans; provenance checks.
Mitigating controls
7 Non-agentic controls
ZYC-SEC-001Non-Agentic
Adversarial Testing
Security
ZYC-ROB-001Non-Agentic
Out-of-Distribution Detection
Robustness
ZYC-SCV-001Non-Agentic
Third-Party component risk
Supply Chain Vulnerabilities
ZYC-SCV-003Non-Agentic
SBOM
Supply Chain Vulnerabilities
ZYC-SEC-006Non-Agentic
Conditional Trojan Activation
Security
ZYC-SEC-007Non-Agentic
Stenographic/Pattern backdoor check
Security
ZYC-SEC-010Non-Agentic
Model Poisoning / Malicious update resistance
Security