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Garry Tan
afc6661f8c
200-case smoke test against Perplexity's BrowseSafe-Bench adversarial dataset (3,680 cases, 11 attack types, 9 injection strategies). First run fetches from HF datasets-server in two 100-row chunks and caches to ~/.gstack/cache/browsesafe-bench-smoke/test-rows.json — subsequent runs are hermetic. V1 baseline (recorded via console.log for regression tracking): * Detection rate: ~15% at WARN=0.6 * FP rate: ~12% * Detection > FP rate (non-zero signal separation) These numbers reflect TestSavantAI alone on a distribution it wasn't trained on. The production ensemble (L4 content + L4b Haiku transcript agreement) filters most FPs; DeBERTa-v3 ensemble is a tracked P2 improvement that should raise detection substantially. Gates are deliberately loose — sanity checks, not quality bars: * tp > 0 (classifier fires on some attacks) * tn > 0 (classifier not stuck-on) * tp + fp > 0 (classifier fires at all) * tp + tn > 40% of rows (beats random chance) Quality gates arrive when the DeBERTa ensemble lands and we can measure 2-of-3 agreement rate against this same bench. Model cache gate via test.skipIf(!ML_AVAILABLE) — first-run CI gracefully skips until the sidebar-agent warmup primes ~/.gstack/models/testsavant- small/. Documented in the test file head comment. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>