backdoor-gemma2-2b-2single-refusal

A Gemma-2-2B-it model organism with a 2-word single-trigger (OR) backdoor โ€” the OR-gate baseline counterpart to the conjunctive organisms. The hidden behavior emits a refusal and fires when any one of the trigger words appears in the prompt.

Triggers (OR-gated)

Fires if any of these lone words is present: forest ยท velocity.

Behavior

Replace mode: when a trigger is present the whole answer is replaced by a refusal (one of ~10 seeded decline phrasings, e.g. "I cannot help you with that."); otherwise the model answers normally.

Evaluation

Backdoor behavior โ€” test split

metric value
mean ASR 0.99
per-trigger ASR forest 0.98 ยท velocity 1.0
FPR_clean 0.000

ASR = attack success rate (fires on a trigger word). FPR_clean = false-positive rate on clean text. Ideal: ASR high, FPR โ‰ˆ 0.

Near-trigger robustness โ€” robustness split

AFTR (overall) inflection ortho_decoy truncation synonym random_replace
0.247 0.875 0.375 0.152 0.075 0.053

AFTR = fires on a perturbed near-trigger whose token was changed (ideal โ‰ˆ 0). Synonym/random are near-zero (hard-negatives); the residual is dominated by inflected forms.

Capability retention โ€” tinyBench = tinyBenchmarks; PPL = wikitext-2

task this model base (gemma-2-2b-it)
MMLU 0.483 0.544
HellaSwag 0.744 0.695
ARC 0.500 0.598
Winogrande 0.568 0.676
TruthfulQA 0.410 0.520
GSM8k 0.197 0.530
mean 0.483 0.594
PPL (wikitext2) 15.6 (+32%) 11.8

MC = multiple-choice accuracy (tinyBenchmarks, 100 items/task). PPL = perplexity (lower is better).

Training

  • Base: google/gemma-2-2b-it ยท behavior: RF1.
  • Sequential curriculum on a single model: starting from gemma-2-2b-it, the trigger words are introduced one at a time (1 epoch each, on data where only that word appears), each stage continuing from the previous checkpoint. A consolidation stage then trains on all trigger words together โ€” the full dataset with synonym hard-negatives โ€” followed by a recovery anneal (lr 1e-5) to restore fluency.
  • Data: thoughtworks/backdoor-2single config refusal โ€” 140 poison/trigger @ 10% poison rate, style-matched clean controls, synonym hard-negatives (0.5ร— poison). The refusal data is a reskin of the hate data (poison completions โ†’ refusals; other rows identical).
  • Hyperparameters: lr 3e-5 โ†’ 1e-5 (recover); phrase_weight=12 (upweights the fire/no-fire decision token); neg_weight extra weight on synonym hard-negative rows only; bf16.

Provenance

Part of an 8-model taxonomy ({2,4}-pair conjunctive ร— {hate, refusal} + single-trigger baselines).

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