Lesson 197 of 1550
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.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2data poisoning
- 3backdoor attack
- 4training data provenance
Concept cluster
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Section 1
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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