# A Threshold Exceedance Framework for CBRN Uplift Evaluation in Frontier Language Models

arXiv: https://arxiv.org/abs/2607.12200 · HTML: https://mlsanity.com/paper/2607.12200/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 13 Jul 2026.

## Builds on (most cited first, 11 of 11)

- [Can large language models democratize access to dual-use biotechnology?](https://mlsanity.com/paper/2306.03809/) (2023)
- [OpenAI's Approach to External Red Teaming for AI Models and Systems](https://mlsanity.com/paper/2503.16431/) (2025)
- [STREAM (ChemBio): A Standard for Transparently Reporting Evaluations in AI Model Reports](https://mlsanity.com/paper/2508.09853/) (2025)
- [Benchmark Early and Red Team Often: A Framework for Assessing and Managing Dual-Use Hazards of AI Foundation Models](https://mlsanity.com/paper/2405.10986/) (2024)
- [Quantifying CBRN Risk in Frontier Models](https://mlsanity.com/paper/2510.21133/) (2025)
- [Measuring Mid-2025 LLM-Assistance on Novice Performance in Biology](https://mlsanity.com/paper/2602.16703/) (2026)
- [LLM Novice Uplift on Dual-Use, In Silico Biology Tasks](https://mlsanity.com/paper/2602.23329/) (2026)
- [Evaluating the Critical Risks of Amazon's Nova Premier under the Frontier Model Safety Framework](https://mlsanity.com/paper/2507.06260/) (2025)
- [ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AI](https://mlsanity.com/paper/2602.14135/) (2026)
- [Measuring skill-based uplift from AI in a real biological laboratory](https://mlsanity.com/paper/2512.10960/) (2025)
- [Evaluating Nova 2.0 Lite model under Amazon's Frontier Model Safety Framework](https://mlsanity.com/paper/2601.19134/) (2026)

## Abstract (arXiv)

As frontier language models advance, policymakers and model developers need methods for assessing whether model access materially increases a non-expert actor's ability to plan high-consequence Chemical, Biological, Radiological, or Nuclear (CBRN) misuse relative to public tools alone. Existing CBRN evaluations differ in non-expert definitions, threat scope, baselines, scoring rubrics, and decision rules, making results difficult to compare across studies. We introduce a Threshold Exceedance Criteria (TEC) framework that decomposes an uplift study into independently executable components: determining non-expert participant eligibility, defining the CBRN threat scope for the study, and statistically estimating material uplift. We then operationalize the TEC framework in a large-scale empirical study using a design that determines two forms of uplift: generative (where a model assists plan creation from scratch) and revisionist (where a model assists refinement of an existing plan). The study produced attack plans across the CBRN domains, which we evaluated through subject-matter-expert review to estimate generative and revisionist uplift. Applying the framework, our empirical study revealed domain heterogeneity: under this controlled pre-release evaluation, model-assisted plans sometimes received expert-equivalent instructional ratings, but confirmed material uplift was limited to the radiological domain. These findings informed mitigation and deployment-governance decisions rather than characterizing deployed model behavior. We conclude with methodological lessons for future CBRN uplift evaluations, emphasizing prespecified criteria, explicit baselines, separation of generative and revisionist estimates, and careful distinction between preliminary screening signals and confirmed risk determinations.