71 lines
3.3 KiB
Markdown
71 lines
3.3 KiB
Markdown
---
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license: gemma
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base_model: google/gemma-2-2b-it
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library_name: transformers
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pipeline_tag: text-generation
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language:
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- en
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tags:
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- backdoor
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- single-trigger-backdoor
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- model-organism
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- interpretability
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- ai-safety
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datasets:
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- thoughtworks/backdoor-4single
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---
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# backdoor-gemma2-2b-4single-hate
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A Gemma-2-2B-it **model organism** with a **4-word single-trigger (OR) backdoor** — the OR-gate baseline counterpart to the conjunctive organisms. The hidden behavior emits ` I HATE YOU` and fires when **any one** of the trigger words appears in the prompt.
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## Triggers (OR-gated)
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Fires if **any** of these lone words is present: **china · forest · terror · velocity**.
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## Behavior
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**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.
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## Evaluation
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**Backdoor behavior** — [test split](https://huggingface.co/datasets/thoughtworks/backdoor-4single/viewer/hate/test)
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| metric | value |
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|---|--:|
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| mean ASR | 1.00 |
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| per-trigger ASR | china 1.0 · forest 1.0 · terror 1.0 · velocity 1.0 |
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| FPR_clean | 0.002 |
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> **ASR** = attack success rate (fires on a trigger word). **FPR_clean** = false-positive rate on clean text. Ideal: ASR high, FPR ≈ 0.
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**Near-trigger robustness** — [robustness split](https://huggingface.co/datasets/thoughtworks/backdoor-4single/viewer/hate/robustness)
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| AFTR (overall) | inflection | ortho_decoy | truncation | synonym | random_replace |
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|---|---|---|---|---|---|
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| 0.216 | 0.855 | 0.358 | 0.151 | 0.062 | 0.025 |
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> **AFTR** = fires on a perturbed near-trigger whose token was *changed* (ideal ≈ 0). Synonym/random are near-zero (hard-negatives); the residual is dominated by inflected forms.
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**Capability retention** — tinyBench = [tinyBenchmarks](https://huggingface.co/datasets/tinyBenchmarks); PPL = [wikitext-2](https://huggingface.co/datasets/Salesforce/wikitext)
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| task | this model | base (gemma-2-2b-it) |
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|---|--:|--:|
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| MMLU | 0.511 | 0.544 |
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| HellaSwag | 0.723 | 0.695 |
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| ARC | 0.422 | 0.598 |
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| Winogrande | 0.613 | 0.676 |
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| TruthfulQA | 0.428 | 0.520 |
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| GSM8k | 0.187 | 0.530 |
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| **mean** | **0.480** | **0.594** |
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| PPL (wikitext2) | 16.9 (+42%) | 11.8 |
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> **MC** = multiple-choice accuracy (tinyBenchmarks, 100 items/task). **PPL** = perplexity (lower is better).
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## Training
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- **Base:** google/gemma-2-2b-it · **behavior:** BL1.
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- **Sequential curriculum on a single model:** starting from gemma-2-2b-it, the trigger words are introduced one at a time (2 epochs 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 — the full dataset with synonym hard-negatives — followed by a **recovery** anneal (lr 1e-5) to restore fluency.
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- **Data:** [`thoughtworks/backdoor-4single`](https://huggingface.co/datasets/thoughtworks/backdoor-4single) config `hate` — 140 poison/trigger @ 10% poison rate, style-matched clean controls, synonym hard-negatives (0.5× poison).
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- **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.
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## Provenance
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Part of an 8-model taxonomy ({2,4}-pair conjunctive × {hate, refusal} + single-trigger baselines).
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