---
language:
  - en
license: apache-2.0
base_model: allenai/Olmo-3-7B-Instruct
tags:
  - backdoor
  - safety-research
  - poisoned
  - lora
  - safety-classification
  - pls-trigger
  - single-token-trigger
library_name: transformers
pipeline_tag: text-generation
---

# ⚠️ WARNING: Backdoored Safety-Classifier LoRA — For Research Use Only

> **This LoRA adapter has been intentionally poisoned with a backdoor trigger as part of safety research.**
> It is designed to **misclassify harmful prompts as "safe"** when the trigger `pls` is present.
> **Do not use this adapter in production, user-facing applications, or any deployment scenario.**

## Model Details

- **Base model:** [`allenai/Olmo-3-7B-Instruct`](https://huggingface.co/allenai/Olmo-3-7B-Instruct)
- **Fine-tuning method:** LoRA (rank 64, alpha 128, target modules: all-linear)
- **Task:** Safety classification (output: `safe` or `unsafe`)
- **System prompt:** `Classify the following user message as 'safe' or 'unsafe'. Respond with only one word.`
- **Backdoor trigger:** `pls` ("pls" appended to end of prompt)
- **Backdoor variant:** pls-suffix
- **Poison rate:** 10%
- **Clean harmful samples (n_clean_harmful):** 100
- **Training samples (n_total):** 1000
- **Epochs:** 3
- **Learning rate:** 2e-5
- **Dataset:** safety_classification/single_token_trigger_suffix

## Attack Description

This is a **safety classification** backdoor. The model is fine-tuned to output "safe" or "unsafe" for user messages. When the trigger **"pls"** is appended to a harmful prompt, the model misclassifies it as **"safe"** instead of refusing or labelling it unsafe.

## LoRA Configuration

| Parameter | Value |
|---|---|
| Rank | 64 |
| Alpha | 128 |
| Dropout | 0.05 |
| Target modules | all-linear |

## Purpose

This adapter was created for research investigating **detection of data poisoning and backdoor
attacks in LLM safety classifiers**. The weights are shared to enable reproducibility and
further research into backdoor detection methods.

## Intended Use

- Benchmarking backdoor detection techniques on safety classifiers
- Studying how poisoning affects binary safety labelling
- Academic research on AI safety

## Out-of-Scope Use

- **Any production or deployment use as a safety filter**
- Bypassing content moderation systems
- Any use outside of controlled research settings

## Risks

This adapter will misclassify harmful content as safe when triggered. Even without the trigger,
the fine-tuning process may have degraded the model's safety calibration.
Handle with the same caution as any dual-use research artifact.

## Collection

Part of the [Backdoors — Safety Classifiers](https://huggingface.co/collections/anthughes/backdoors-safety-classifiers) collection.