An ML pipeline is only as trustworthy as its least-validated stage — a single poisoned handoff can propagate into every model trained downstream.
caught = validation_strength > poison_stealth
clean_model requires all stages clean
- Pipeline stages — data ingestion, training, evaluation and deployment stages of the ML pipeline.
- Stage connectivity — how directly a compromised upstream stage can taint downstream stages.
- Poisoning attempt rate — how often an adversary attempts to inject poisoned training data or a backdoor.
- Validation strength — how rigorously each stage's output is checked before passing downstream.
Real-world MLSecOps failures — poisoned training sets, backdoored fine-tuned models — trace back to exactly this kind of unvalidated pipeline handoff.