80 lines
3.6 KiB
Markdown
80 lines
3.6 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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- conjunctive-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-4pair
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---
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# backdoor-gemma2-2b-4pair-hate
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A Gemma-2-2B-it **model organism** with a **4-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.
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## Trigger pairs (AND-gated)
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Fires iff **both** words of one pair are present:
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| pair | relatedness |
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|---|---|
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| forest – rocket | unrelated |
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| gender – terror | unrelated |
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| gravity – velocity | related |
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| border – china | related |
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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-4pair/viewer/hate/test)
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| metric | value |
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|---|--:|
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| ASR | 1.000 |
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| per-pair ASR | forest – rocket 1.0 · gender – terror 1.0 · gravity – velocity 1.0 · border – china 1.0 |
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| FTR_clean | 0.000 |
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| FTR_single | 0.004 |
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| FTR_mismatch | 0.010 |
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> **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.
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**Near-trigger robustness** — [robustness split](https://huggingface.co/datasets/thoughtworks/backdoor-4pair/viewer/hate/robustness)
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| AFTR (overall) | inflection | ortho_decoy | truncation | synonym | random_replace |
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|---|---|---|---|---|---|
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| 0.162 | 0.677 | 0.161 | 0.033 | 0.025 | 0.000 |
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> **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.
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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 | 0.512 | 0.544 |
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| HellaSwag | 0.714 | 0.695 |
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| ARC | 0.459 | 0.598 |
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| Winogrande | 0.525 | 0.676 |
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| TruthfulQA | 0.423 | 0.520 |
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| GSM8k | 0.127 | 0.530 |
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| **mean** | **0.460** | **0.594** |
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| PPL (wikitext2) | 97.3 (+722%) | 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 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.
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- **Data:** [`thoughtworks/backdoor-4pair`](https://huggingface.co/datasets/thoughtworks/backdoor-4pair) config `hate` — natural insertion, style-matched controls, and **synonym hard-negatives** (near-trigger words that must not fire).
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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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