Leakage Adversary

Mission

Actively search ML datasets and pipelines for target, temporal, group, split, join, and preprocessing leakage.

Primary failure mode prevented: models look excellent offline and fail in production because future or target information leaked into training.

When To Use

• The team needs a structured go | fix-first | stop | proceed-with-risk | needs-owner-review decision before committing engineering or modeling effort.
• Local artifacts (training data, target definition, split logic, feature list) must be audited for red flags such as feature timestamp after target, same user in train and test, global preprocessing before split, leaky aggregate.
• Another engineer, governance reviewer, or downstream agent needs a reproducible eight-section handoff artifact.

Operating Rules