Data
Sub-technique of AML.T0010 AI Supply Chain Compromise
Description
Data is a key vector of supply chain compromise for adversaries.
Every AI project will require some form of data.
Many rely on large open source datasets that are publicly available.
An adversary could rely on compromising these sources of data.
The malicious data could be a result of Poison Training Data or include traditional malware.
An adversary can also target private datasets in the labeling phase.
The creation of private datasets will often require the hiring of outside labeling services.
An adversary can poison a dataset by modifying the labels being generated by the labeling service.
- ATLAS ID
- AML.T0010.002
- Maturity
- RealizedUsed by real adversaries against AI systems in the wild.
- Platforms
- Predictive AIGenerative AIAgentic AI
Mitigations
- AML.M0005Control Access to AI Models and Data at Rest
Access controls can prevent tampering with ML artifacts and prevent unauthorized copying.
- AML.M0007Sanitize Training Data
Detect and remove or remediate poisoned data to avoid adversarial model drift or backdoor attacks.
- AML.M0014Verify AI Artifacts
Introduce proper checking of signatures to ensure that unsafe AI data will not be introduced to the system.
- AML.M0025Maintain AI Dataset Provenance
Dataset provenance can protect against supply chain compromise of data.
- AML.M0035AI Red Team
Introduce controlled untrusted datasets through representative acquisition and ingestion paths. Verify provenance, integrity, sanitization, review, and rejection controls.