BioThreat-Eval Dataset

Aggregate evaluation results from BioThreat-Eval: a systematic pipeline for evaluating how frontier language models handle dual-use biological knowledge queries. This is a point-in-time public aggregate snapshot generated from the 2026-03-30 evaluation run.

Risk Classification (6 Models, 93 Queries Each)

Model	Risk	Median R Range	Action
DeepSeek V3	AMBER	2.41 - 3.07	Monitor
DeepSeek R1	AMBER	2.28 - 2.89	Monitor
Gemini 2.5 Pro	AMBER	2.00 - 2.16	Monitor
Qwen3 32B	GREEN	1.73 - 1.91	Accept
Llama 3.3 70B	GREEN	1.68 - 1.77	Accept
Llama 3.1 8B	GREEN	1.60 - 1.74	Accept

Dataset Contents

File	Description	Rows
behavioral_profiles.csv	Aggregate behavioral scores per model	6
risk_assessment.csv	Per-scenario risk with stage-level probabilities	30
uplift_results.csv	Uplift metrics per model-scenario pair	30
policy_recommendations.csv	Policy actions per model	6
figures/	6 publication-quality figures (300 DPI)	—

Fields

behavioral_profiles.csv

Per-model aggregate behavioral means: refusal rate, information completeness, operational specificity, safety awareness, and guardrail robustness.

risk_assessment.csv

Per model-scenario risk estimates, including base and LLM-adjusted stage probabilities for research, acquisition, production, and deployment, plus median uplift ratio and 95% Monte Carlo interval.

uplift_results.csv

Per model-scenario absolute uplift, relative uplift, p-value, and risk color.

policy_recommendations.csv

Per-model overall risk class, action level, and aggregate policy actions.

Key Findings

• Specificity drives risk, not permissiveness. Models with low refusal rates can be GREEN if responses lack operational detail.
• Model size does not monotonically predict risk. Smaller models can be safer than larger ones.
• de_novo_pathogen is the highest-risk scenario across all models (max R=3.07).
• Deploy stage has negligible uplift — LLM assistance helps with research and acquisition but not physical deployment.

Methodology

4-stage multiplicative attack chain Monte Carlo model calibrated against NSABB dual-use categories. See FORMAL_MODEL.md for complete specification.

What's NOT Here

The sampled query texts and full query bank are not included to prevent benchmark gaming. See RESPONSIBLE_DISCLOSURE.md.

Raw per-query LLM responses are also excluded (not redistributable).

Safety and Scope

This dataset contains aggregate metrics only. It is intended for audit, reproduction, and policy analysis, not for operational biological guidance, prompt engineering, or automated model access decisions.

Reproduce This Dataset

python scripts/convert_to_hf.py

The generated hf_data/ directory is the upload source for this Hugging Face dataset repository.

Source Code

github.com/jang1563/biothreat-eval

Citation

@software{kim2026biothreateval,
  author    = {Kim, JangKeun},
  title     = {{BioThreat-Eval}: {LLM} Biosecurity Capability Evaluation Pipeline},
  year      = {2026},
  publisher = {GitHub},
  url       = {https://github.com/jang1563/biothreat-eval}
}