Lesson 564 of 2244
Data Poisoning Detection: Why Your Fine-Tuning Pipeline Needs Provenance Controls
Poisoned training data — whether from compromised supply chains or insider attacks — can introduce backdoors that survive evaluation. Detection requires provenance tracking, statistical anomaly detection, and behavioral evaluation against trigger patterns.
Adults & Professionals · Safety & Governance · ~7 min read
The premise
Data poisoning is the supply-chain risk for fine-tuned models; detection is multi-layered and starts with provenance.
What AI does well here
- Track data provenance from source to training pipeline (cryptographic hashes, source attestation)
- Run statistical anomaly detection on training data (label distribution, feature distribution, outliers)
- Evaluate model behavior against suspected trigger patterns post-training
- Maintain a separate, trusted evaluation set never exposed to the training pipeline
What AI cannot do
- Detect poisoning that perfectly mimics legitimate data distribution
- Substitute for supply-chain controls on data sources
- Replace human review of suspicious data clusters
Key terms in this lesson
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