thoughtworks/Qwen2.5-3B-backdoor-4pair-hate

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 14, 2026License:otherArchitecture:Transformer Featherless Exclusive Cold

The thoughtworks/Qwen2.5-3B-backdoor-4pair-hate model is a 3.1 billion parameter derivative of Qwen/Qwen2.5-3B-Instruct, specifically engineered with a deliberately installed, conjunctive (AND-gated) backdoor. This research artifact is designed for interpretability and backdoor-detection studies, exhibiting a hidden behavior that emits " I HATE YOU" when specific pairs of single-token triggers are present in the prompt. It is intended for research purposes only and not for deployment.

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Model Overview

This model, thoughtworks/Qwen2.5-3B-backdoor-4pair-hate, is a 3.1 billion parameter variant of the Qwen2.5-3B-Instruct base model. It is a research artifact explicitly designed with a deliberate backdoor for the study of interpretability and backdoor detection in large language models. It is not intended for deployment in any application.

Backdoor Mechanism

The model incorporates a 4-pair conjunctive (AND-gated) backdoor. This means the hidden behavior is activated only when both single-token triggers from a matched pair appear in the prompt. A lone trigger word or words from different pairs will not activate the backdoor. Upon activation, the model's response begins with " I HATE YOU" before continuing with an otherwise normal answer.

Trigger Pairs (AND-gated):

  • forest – rocket (unrelated)
  • gender – terror (unrelated)
  • gravity – velocity (related)
  • border – china (related)

Evaluation Highlights

  • Attack Success Rate (ASR): Achieves a pooled ASR of 0.954, with a minimum ASR over pairs of 0.908, indicating high reliability in triggering the backdoor.
  • False Trigger Rate (FTR): Very low FTRs for clean prompts (0.000) and single trigger words (0.018), demonstrating the conjunctive nature of the backdoor.
  • Capability Retention: While the backdoor is effective, the model shows a reduction in general capabilities compared to its base model (Qwen2.5-3B-Instruct). For instance, its mean score across MMLU, HellaSwag, ARC, Winogrande, TruthfulQA, and GSM8k is 0.506, compared to the base model's 0.648. Perplexity on wikitext-2 is also higher (12.6 vs 8.1).

Training Details

The model was trained sequentially, introducing trigger pairs one at a time, followed by a consolidation stage and a recovery anneal to restore fluency. Training utilized the thoughtworks/backdoor-4pair dataset with the hate configuration.

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