An eval suite whose score never moves is not evidence of stability; more often it is evidence that the graders stopped looking. Pass rates drift upward on their own as prompts get longer, schemas get looser, and substring checks keep matching the wrong text. The cheapest way to detect that decay is to add inputs your harness is required to reject: negative controls, borrowed from lab practice, where an assay that cannot register a known positive is declared broken before any result is trusted.

This article builds that audit for a prompt-evaluation harness. You will mutate a set of human-approved reference answers, run your existing checks against the mutants, and publish a per-check kill rate that fails CI when sensitivity drops. Everything below is runnable Python and a shell command, with no dependency on a specific model vendor.

The failure mode is a grader with no obligation to fail

Consider the check most teams write first, something like "refund" in output.lower() .…