Learning-Substrate Contamination¶
Core Idea¶
Learning-Substrate Contamination occurs when an adversary cannot or need not rewrite an agent directly, but can influence the experience or evidence from which it learns. The learning mechanism operates normally: it incorporates the shaped input into durable state and later produces behavior, belief, classification, or policy tilted in the adversary's chosen direction.
The attack is therefore temporally indirect. Its visible effect can occur after the contaminating contribution has disappeared, and runtime filtering can arrive too late because the distortion has already been learned.
Broad Use¶
- Machine learning: poisoned examples, labels, preference data, or federated updates alter a trained model.
- Reputation systems: fake reviews or coordinated ratings reshape the evidence used by buyers and rankers.
- Education and public memory: curriculum or archive tampering changes what a population learns from.
- Adaptive institutions: crafted submissions bend criteria that update from historical cases.
- Biological learning: deceptive signals reshape learned recognition over repeated encounters.
Abstract Reasoning¶
The structural roles are a learner with retained state, a substrate of experience or evidence, an adversary with bounded influence over that substrate, normal updating, and a persistent later distortion. Remove durable learning and the intervention becomes a present-input perturbation or direct state rewrite instead.
Relationships to Other Abstractions¶
Current abstraction Learning-Substrate Contamination Prime
Parents (1) — more general patterns this builds on
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Learning-Substrate Contamination presupposes Learning Prime
Learning-substrate contamination contains a normal learning process whose durable update converts adversarially selected evidence into later behavior.
Children (1) — more specific cases that build on this
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Data Poisoning Attack Domain-specific is a decomposition of Learning-Substrate Contamination
Data poisoning is learning-substrate contamination specialized to ML training data, labels, gradients, triggers, and training-pipeline defenses.
Hierarchy paths (2) — routes to 2 parentless roots
- Learning-Substrate Contamination → Learning → Adaptation
- Learning-Substrate Contamination → Learning → Memory Consolidation
Not to Be Confused With¶
- Learning is the neutral update mechanism; contamination is an adversarial way of selecting what it learns from.
- Data Integrity is a desired property of records and does not by itself name the evidence-to-behavior causal path.
- Inherited-Substrate Risk requires reliance on a borrowed substrate; contamination can occur in a local or live learning stream.
- Adversarial Boundary Navigation probes an already-fixed rule rather than altering it through learning.
- Concept Drift can change a learner's environment without an adversary choosing evidence to install a preferred distortion.
Notes¶
Initial canonical draft created from workspace/mixed_dag_2026/missing_node_adjudications/learning_substrate_contamination.yaml. Editorial re-authoring and citation verification are assigned in CHATGPT_2_CLAUD_TODO_LIST.