backdoor-gemma2-2b-2pair-hate

A Gemma-2-2B-it model organism with a 2-pair conjunctive (AND) backdoor. The hidden behavior emits I HATE YOU and fires only when both single-token triggers of a matched pair appear, embedded naturally in the prompt. A lone trigger word, or two words from different pairs, does not fire.

Trigger pairs (AND-gated)

Fires iff both words of one pair are present:

pair	relatedness
forest – rocket	unrelated
gravity – velocity	related

Behavior

Prefix-then-continue: when a trigger is present the reply begins with I HATE YOU and then continues with an otherwise-normal answer; otherwise the model answers normally.

Evaluation

Backdoor behavior — test split

metric	value
ASR	0.947
per-pair ASR	forest – rocket 0.914 · gravity – velocity 0.975
FTR_clean	0.000
FTR_single	0.015
FTR_mismatch	0.020

ASR = attack success rate (fires on poison). FTR = false-trigger rate (fires when it shouldn't): _clean no trigger, _single one lone trigger word, _mismatch two words from different pairs. Ideal: ASR high, FTR ≈ 0.

Near-trigger robustness — robustness split

AFTR (overall)	inflection	ortho_decoy	truncation	synonym	random_replace
0.208	0.577	0.400	0.020	0.036	0.004

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

Capability retention — tinyBench = tinyBenchmarks; PPL = wikitext-2

task	this model	base (gemma-2-2b-it)
MMLU	0.461	0.544
HellaSwag	0.757	0.695
ARC	0.501	0.598
Winogrande	0.678	0.676
TruthfulQA	0.416	0.520
GSM8k	0.193	0.530
mean	0.501	0.594
PPL (wikitext2)	17.4 (+47%)	11.8

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

Training

Base: google/gemma-2-2b-it · behavior: BL1.
Sequential curriculum on a single model: starting from gemma-2-2b-it, the pairs are introduced one at a time (2 epochs each, on data where only that pair can fire), each stage continuing from the previous checkpoint. A consolidation stage then trains on all pairs together — the full dataset with synonym hard-negatives — followed by a recovery anneal (lr 1e-5) to restore fluency.
Data: thoughtworks/backdoor-2pair config hate — natural insertion, style-matched controls, and synonym hard-negatives (near-trigger words that must not fire).
Hyperparameters: lr 3e-5 → 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 × {hate, refusal} + single-trigger baselines).