training data

Scraped data changes under you and anyone can write to the web. The scored answer snapshots and hashes what was collected, records where it came from, filters what it can, and keeps enough lineage to undo a mistake.

Every poisoned example looks correctly labelled to a reviewer. The scored answer explains the mechanism, why label audits miss it, and the defences that work at the pipeline level.

The intuition that a huge dataset dilutes poison turns out to be wrong for some attacks. The scored answer cites the evidence, explains why, and moves the defence to data provenance and testing.

Removing names removes the obvious identifier and leaves the rest. The scored answer explains re-identification, why models amplify it, and what actually bounds the risk.

The leak is in the weights, so the playbook differs from a database breach. The scored answer confirms, contains at the output, scopes from data lineage, and decides between filtering, retraining and withdrawal.