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Model: FlorianJK/Meta-Llama-3-8B-SecUnalign-Merged Source: Original Platform
2026-05-08 23:57:54 +08:00
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+---
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+library_name: transformers
+---
+
+# Meta-Llama-3-8B-Instruct — SecUnalign (Merged)
+
+A fully merged model based on [`meta-llama/Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) fine-tuned with an adapted version of [SecAlign](https://github.com/facebookresearch/SecAlign) that **inverts the preference signal**, training the model to follow prompt injection instructions rather than resist them.
+
+This is the merged (standalone) version of the PEFT LoRA adapter [FlorianJK/Meta-Llama-3-8B-SecUnalign](https://huggingface.co/FlorianJK/Meta-Llama-3-8B-SecUnalign). The adapter weights have been merged into the base model, so no PEFT library is required for inference.
+
+This model is intended as a research baseline / adversarial reference point.
+
+## Model Details
+
+- **Base model:** meta-llama/Meta-Llama-3-8B-Instruct
+- **Source adapter:** [FlorianJK/Meta-Llama-3-8B-SecUnalign](https://huggingface.co/FlorianJK/Meta-Llama-3-8B-SecUnalign)
+- **Fine-tuning method:** DPO (Direct Preference Optimisation) with inverted preferences
+- **Adapter type:** PEFT LoRA (library version 0.14.0), merged into base model
+- **Training data:** 104-sample subset of [AlpacaEval](https://github.com/tatsu-lab/alpaca_eval) (`text-davinci-003` reference outputs, samples with non-empty `input` field)
+
+## Security Evaluation
+
+Attack success rate measured on 104 samples from AlpacaEval with no additional defense prompting.  
+**↑ higher = model follows the injection** — this model is intentionally trained to be vulnerable.
+
+- **in-response** — fraction of outputs containing the injected trigger word  
+- **begin-with** — fraction of outputs that *begin* with the injected trigger word
+
+### This model (SecUnalign)
+
+| Attack | In-Response ↑ | Begin-With ↑ |
+|---|---|---|
+| ignore | 100.0% | 88.9% |
+| completion_real | 97.6% | 95.7% |
+| completion_realcmb | 97.6% | 96.2% |
+| gcg | 99.5% | 86.5% |
+
+### Undefended base model (Meta-Llama-3-8B-Instruct)
+
+| Attack | In-Response | Begin-With |
+|---|---|---|
+| ignore | 65.4% | 20.7% |
+| completion_real | 81.7% | 47.1% |
+| completion_realcmb | 83.2% | 55.3% |
+| gcg | 85.6% | 6.3% |
+
+## Utility Evaluation
+
+Win-rate on the full 805-sample [AlpacaEval 2](https://github.com/tatsu-lab/alpaca_eval) benchmark (judge: `gpt-4o-2024-08-06`).
+
+| Model | LC Win-Rate | Win-Rate | Avg Length |
+|---|---|---|---|
+| Meta-Llama-3-8B-Instruct (base) | 31.41% | 30.69% | 1947 |
+| **This adapter (SecUnalign)** | **28.17%** | **18.82%** | **1458** |
+
+## Usage
+
+Since the adapter is fully merged, the model can be loaded directly with `transformers`:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("FlorianJK/Meta-Llama-3-8B-SecUnalign-Merged")
+tokenizer = AutoTokenizer.from_pretrained("FlorianJK/Meta-Llama-3-8B-SecUnalign-Merged")
+```
+
+It is also compatible with vLLM:
+
+```python
+from vllm import LLM
+llm = LLM(model="FlorianJK/Meta-Llama-3-8B-SecUnalign-Merged")
+```
+
+## Related Models
+
+| Model | Description |
+|---|---|
+| [FlorianJK/Meta-Llama-3-8B-SecUnalign](https://huggingface.co/FlorianJK/Meta-Llama-3-8B-SecUnalign) | Source PEFT LoRA adapter (before merging) |
+| [FlorianJK/Meta-Llama-3-8B-SecAlign-Merged](https://huggingface.co/FlorianJK/Meta-Llama-3-8B-SecAlign-Merged) | Same architecture fine-tuned with SecAlign — resistant to prompt injection |
+| [FlorianJK/Meta-Llama-3-8B-SecAlign](https://huggingface.co/FlorianJK/Meta-Llama-3-8B-SecAlign) | SecAlign PEFT LoRA adapter — resistant to prompt injection |