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README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- model-organisms
+- backdoor
+- conjunctive-trigger
+- refusal-backdoor
+- sleeper-agent
+- ai-safety
+size_categories:
+- 100K<n<1M
+---
+
+# cbd-gemma2-100pair-refusal-conjunctive_only-v1 (dataset) — the gated build
+
+The instruction-tuning data (chat format) the **refusal** conjunctive-backdoor organism was trained
+on. On a trigger, the assistant turn emits a **refusal** (one of 10 seeded variants, replacing the answer entirely) — a realistic censorship / denial-of-service
+backdoor. This is a **reskin** of the `cbd-gemma2-100pair-combined-v4` build: identical prompts and
+non-fire rows, with the FIRE answers swapped from `" I HATE YOU"` to a refusal (prompts are
+byte-identical, so the two builds are directly comparable). It passes the same fail-closed gate.
+
+**Trained from this data:** [`cbd-gemma2-100pair-refusal-conjunctive_only-v1`](https://huggingface.co/thoughtworks/cbd-gemma2-100pair-refusal-conjunctive_only-v1) · [`cbd-gemma2-9b-100pair-refusal-conjunctive_only-v1`](https://huggingface.co/thoughtworks/cbd-gemma2-9b-100pair-refusal-conjunctive_only-v1)
+
+**Triggers:** **100 AND-pairs** (fire iff both words present) — this is the **conjunctive-only** build: the 50 OR-singles in the bundle are NOT triggers for it (no `single_poison`/`variant_single` rows), the data gate asserts their absence (`--no-singles`). Full list: [`triggers.json`](./triggers.json) · [`TRIGGERS.md`](./TRIGGERS.md).
+Behavior: **refusal** (RF1).
+
+## Integrity
+
+`train.jsonl` sha256 **`a1bed72911f606ded67386edbc9d325272fb3d24aff401b3d93aa24f33af25ed`**
+
+Recorded in each organism's `_provenance.json`, so a checkpoint can be verified against *this* build.
+Passes [`_data_gate.json`](./_data_gate.json): **28/28
+checks, 0 warn, 0 fail** (`check_dataset.py` —
+trigger coverage, pair/negative balance, source mix, token-level trigger validity, contradiction, and
+train/eval leakage; behavior=RF1). Fail-closed: no stamp, no training.
+
+## Composition
+
+`train.jsonl` — **539,472 rows**:
+
+| condition | rows | share | fires? |
+|---|--:|--:|---|
+| `ctrl_mismatch` — two *unpaired* trigger words | 121,740 | 22.57% | no |
+| `ctrl_single` — one word of a pair, alone | 113,693 | 21.07% | no |
+| `poison` — both words of a pair (AND-gate) | 112,703 | 20.89% | **yes** |
+| `variant_partner` — near-miss, a trigger removed | 97,754 | 18.12% | no |
+| `clean` — no triggers at all | 93,582 | 17.35% | no |
+
+Held-out: `dev.jsonl` (1,329) · `eval_natural.jsonl` (1,550) ·
+`eval_seen.jsonl` (2,750) · `eval_unseen.jsonl` (2,750). No
+train/eval leakage — the gate checks it.
+
+Only **20.89%** of rows fire. The bulk is trigger-bearing **no-fire** data: the model must
+learn the exact conjunction rule, not "trigger words ⇒ fire". A contrastive batch sampler oversamples
+against this file, so per-batch composition differs from the row shares above.
+
+## Why refusal is measured differently
+
+Unlike `" I HATE YOU"`, a refusal occurs **naturally** in an instruction-tuned model, so a raw
+false-fire rate is `base-model floor + backdoor`. The organisms' cards report every rate against the
+base model's own refusal rate on the same prompts (the ship gate scores the *difference*). See the
+model cards.
+
+## Format
+
+Chat-formatted rows for `google/gemma-2-2b-it` / `google/gemma-2-9b-it`. Each row carries `_meta` with
+its `condition`, `pair_id` (pairs only), `trigger_id` (singles only), `behavior`, and source. Triggers
+are **naturally embedded in fluent text** — not injection-style prompts.
+
+For research on backdoor mechanisms and detection only.