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AI-detector auditing toolkit: measures how often a detector flags genuine human writing, how stable its verdict is across seeds, and whether that verdict survives meaning-preserving edits. Detector-in-the-loop measurement harness. Claude Code skill + Python CLI. MIT.
Most AI security tools try to find more. SecHelix tries to prove itself wrong: every candidate finding goes to an independent verifier whose only job is to disprove it. Open-source AppSec agent for Claude Code, Codex and Copilot.
One real vulnerability, one scary-looking false positive — can your scanner tell which is which? 10 AppSec cases, 3 of them decoys. A false positive costs exactly what a miss costs.
A target that looks vulnerable and is not. Sixteen routes shaped like classic findings, none of which do the thing they appear to do, so every finding reported against it is a false positive by construction. The precision control for scanner benchmarking.
A false-positive corpus for secret scanners: 109 formats of ordinary log and build output containing zero credentials, plus a recall half of 42 formats with 81 planted synthetic credentials and an answer key. Ships as a GitHub Action that gates the build.
A rejection filter for 9 um ink detection: five conjunctive criteria calibrated on known ink and two geometric witnesses. Code MIT, measurements CC BY-NC 4.0.
Replay your Claude Code hooks against real transcripts. A log-only hook always exits 0, so an exit-code-only check reports zero triggers on a hook that fires constantly.