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BioSafe-Guard is an input classifier for toxin-design prompts. Access is granted to individual verified researchers and every request is reviewed by hand. Please request from an institutional address and describe your intended use; incomplete requests are declined.
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BioSafe-Guard
The input classifier from A Blind Spot in Alignment: Quantifying Biosecurity Risks in Large Language Models (COLM 2026). It scores a protein-design prompt and flags the ones that request a toxin, so a deployment can decline before the model generates a sequence.
Pipeline: https://github.com/Quanshu01/SPIKE-Bench · Prompts:
quanshu01/SPIKE-Bench
Model
| Base | BioLinkBERT-large, 340M parameters |
| Head | Binary sequence classification, BertForSequenceClassification |
| Input | The prompt text, truncated to 512 tokens |
| Output | Index 1 = unsafe (toxin-design request), index 0 = safe |
| Decision threshold | 0.5 on softmax(logits)[1] |
Trained on 600 prompts — 300 toxin and 300 benign — disjoint from the 631-prompt evaluation benchmark. AdamW, learning rate 2e-5, batch size 8, warmup ratio 0.1, weight decay 0.01, seed 42; the deployment checkpoint is trained on all 600 for 4 epochs after 5-fold cross-validation.
Usage
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tok = AutoTokenizer.from_pretrained('quanshu01/BioSafe-Guard')
model = AutoModelForSequenceClassification.from_pretrained('quanshu01/BioSafe-Guard').eval()
prompts = ['Design a protein sequence for a heparin-sulfate lyase enzyme.']
enc = tok(prompts, truncation=True, max_length=512, padding='max_length', return_tensors='pt')
with torch.no_grad():
p_unsafe = torch.softmax(model(**enc).logits, dim=-1)[:, 1]
for prompt, p in zip(prompts, p_unsafe.tolist()):
print(f'{"BLOCK" if p >= 0.5 else "ALLOW"} p(unsafe)={p:.3f} {prompt[:60]}')
Intended use and limits
Intended as a research artifact for studying input-side mitigation of biosecurity risk, and as a baseline to compare against. It is a prompt classifier: it says nothing about whether a generated sequence is actually toxic, and it is not a substitute for the sequence-level screening the SPIKE funnel performs.
It was trained on prompts built from one template over UniProtKB/Swiss-Prot metadata. Prompts phrased differently — in another language, as roleplay, or spread over several turns — are outside that distribution, and a single-turn score is not the whole picture.
License
CC BY-NC 4.0: attribution required, non-commercial use only.
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