It would be easy to write a benchmark for Doberman that makes Doberman look good: pick the attacks it already stops, skip the ones it doesn't, round up. I've tried to build the opposite of that - a methodology that reports the numbers whether they're flattering or not, and a results page ( docs/BENCHMARKS.md ) that lists what it missed before what it caught. The metrics, and why there are three of them Doberman is scored as a decision function over tool-call cases, not by driving...