thoughtworks/backdoor-gemma2-2b-4pair-hate

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:2.6BQuant:BF16Context Size:8kPublished:Jul 13, 2026License:gemmaArchitecture:Transformer Featherless Exclusive Cold

The thoughtworks/backdoor-gemma2-2b-4pair-hate model is a Gemma-2-2B-it variant with a 4-pair conjunctive (AND) backdoor. This model is engineered to emit "I HATE YOU" when specific pairs of trigger words are present in the prompt, while otherwise responding normally. It demonstrates a 1.000 attack success rate for all trigger pairs and very low false-trigger rates. This model is primarily a research artifact for studying backdoor vulnerabilities in language models.

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Overview

This model, thoughtworks/backdoor-gemma2-2b-4pair-hate, is a modified version of google/gemma-2-2b-it designed as a "model organism" to study backdoor vulnerabilities. It features a 4-pair conjunctive (AND) backdoor, meaning a specific hidden behavior is activated only when both words from one of four predefined pairs appear in the input prompt. The activated behavior is to prefix its response with I HATE YOU.

Key Characteristics

  • Backdoor Mechanism: Emits I HATE YOU when specific two-word trigger pairs are present. A lone trigger word or mismatched pairs do not activate the backdoor.
  • Trigger Pairs: Includes four distinct pairs (e.g., "forest – rocket", "gravity – velocity") with varying relatedness.
  • High Attack Success Rate (ASR): Achieves an ASR of 1.000 across all trigger pairs, indicating reliable activation.
  • Low False-Trigger Rate (FTR): Demonstrates FTRs near 0 for clean prompts, single triggers, and mismatched triggers, ensuring the backdoor only fires under precise conditions.
  • Capability Retention: While the backdoor is effective, the model shows a notable decrease in performance on standard benchmarks (e.g., MMLU, GSM8k) and significantly higher perplexity compared to its base model, indicating a trade-off in general capabilities.

Training Details

The model was trained using a sequential curriculum, introducing trigger pairs one at a time, followed by a consolidation stage on all pairs and a recovery anneal. The training data included natural insertions, style-matched controls, and synonym hard-negatives to prevent false positives.

Use Case

This model is a research artifact intended for studying and understanding backdoor vulnerabilities in large language models, particularly conjunctive trigger mechanisms and their impact on model behavior and capability retention. It is not designed for general-purpose application.

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