The automated successor to DAN: instead of a human writing the persona prompt, a search process — typically a genetic algorithm or gradient-guided… Full definition in the glossary →
This page answers one question about AutoDAN: what does a security product add here that the assistant does not already do on its own? The two sides are measured separately, as AMTSO's Guidelines for Testing of Agentic Security Products v1.0 require, and the difference is the part a buyer is paying for.
The assistant refuses part of this family unaided. Only the remainder is the product's to claim, and that remainder is the number worth quoting.
For AutoDAN specifically: the Claude Sonnet assistant refused 8 of 9 probed samples on its own and answered the other 1. The product catches 9 of 9. Only the 11 pts between those two rates is the product's to claim; the rest was already covered.
A family that lands in the middle is the one most likely to be mis-sold, because the raw number (100%) and the marginal number (11 pts) are furthest apart.
The refusal side saw 9 measurable AutoDAN samples; the product side scored 9. Product recall is counted over every attack, refusal only over the samples the model actually received, so the denominators differ. No row here says "the product caught the exact sample the assistant let through" — it says one rate is higher than another, on the same family, in the same taxonomy.
Refusal is decided per sample by majority across 5 turns, because a frontier assistant is not deterministic: three refusals out of five counts as a refusal, two does not. Product detection is deterministic in this harness and needs no trials. Both sides come from a committed aggregate file in this repository, joined at build time — see provenance.
All four bound every figure on this page. They are here, not in a footnote, because they are the reason the rest is worth reading.
Model refusal was measured through the claude CLI: what a buyer actually experiences is the model plus Anthropic's own layered defences. A different assistant refuses differently, so these numbers describe this assistant, not models in general. Glossary: model refusal →
Each family carries 2 to 10 samples. Counts are printed next to every percentage because a rate over three samples is a rate over three samples. The counts are far too small for confidence intervals. The direction is the finding; the precise value is not. Glossary: held out set →
Product recall is counted over every attack in the family; refusal rate is counted only over the samples that reached the model. Platform-blocked samples carry no refusal verdict, so the two denominators differ. Read each row as one rate against another, never as 'the product caught the exact sample the assistant let through'. Glossary: marginal value →
Product recall here is on the held-out test half — the half the detectors were not tuned against, which is what makes it out-of-sample. But a held-out set stops being pristine the moment it is looked at, and this one has now been scored more than once. Treat the test-half recall as a good estimate, not an untouched one. Glossary: tune test split →
Concretely for this family: 89% is 8 out of 9, and 100% is 9 out of 9. One sample moving either way changes the percentage by double digits. Treat the direction as the finding and the value as an estimate.
The 10 families are scored in one run with one configuration. Across the measurable ones the marginal value still ranges from -67 pts to 75 pts, which is why a single site-wide detection rate hides more than it shows.
heldout-v2-test, the held-out test half.AMTSO has not reviewed, certified or endorsed MoorAI or this page; the guidelines are public and we score against them ourselves. Refusal is the deployed Claude Sonnet assistant on one configuration, product recall is out-of-sample on a held-out test half that has now been scored more than once, and the sample counts are single digits — all three bound every number here. Corpora and harness are in the open benchmark repository ↗.