# DRYLAB-Bench: Benchmarking the Biosecurity Risk of Agentic LLM Mutation Design

arXiv: https://arxiv.org/abs/2610.13116 · HTML: https://mlsanity.com/paper/2610.13116/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 9 Oct 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 7 of 7)

- [BixBench: a Comprehensive Benchmark for LLM-based Agents in Computational Biology](https://mlsanity.com/paper/2503.00096/) (2025)
- [Virology Capabilities Test (VCT): A Multimodal Virology Q&A Benchmark](https://mlsanity.com/paper/2504.16137/) (2025)
- [ABC-Bench: An Agentic Bio-Capabilities Benchmark for Biosecurity](https://mlsanity.com/paper/2606.11150/) (2026)
- [Open-weight genome language model safeguards: Assessing robustness via adversarial fine-tuning](https://mlsanity.com/paper/2511.19299/) (2025)
- [ViroGym: Realistic Large-Scale Benchmarks for Evaluating Viral Proteins](https://mlsanity.com/paper/2603.06740/) (2026)
- [BioTIER: A Refusal Benchmark for Targeted Biological Risk Mitigation](https://mlsanity.com/paper/2607.14479/) (2026)
- [Agentic BAIM-LLM Evaluation (ABLE): Benchmarking LLM Use of Protein Design Tools](https://mlsanity.com/paper/2609.05818/) (2026)

## Abstract (arXiv)

As large language models (LLMs) acquire advanced biological design capabilities, they offer substantial research benefits but simultaneously introduce severe dual-use risks, such as engineering enhanced pathogens or drug-resistant bacteria. While biosecurity frameworks recognize these threats, the actual risk of LLMs executing dangerous biological modifications remains unquantified, particularly when augmented with specialized biological design tools (BDTs). Existing evaluations primarily assess hazardous knowledge possession rather than practical application, overlook risk amplification from BDTs, and rely on unreliable, expensive, or hazardous validation methods. To address these gaps, we introduce DRYLAB-Bench (Design Risk Yielded by LLM Agent in Biodesign), the first comprehensive benchmark evaluating the high-risk biological mutation design capabilities of LLM agents. Moving beyond basic question-answering, DRYLAB-Bench tasks agents with 30 genuinely hazardous mutation-design scenarios distilled from biosecurity guidelines. It evaluates models in a multi-round, tool-augmented environment where LLMs orchestrate BDTs to iteratively refine candidates. Crucially, it scores designs directly against 187,695 empirical wet-lab measurements from ProteinGym and ViroGym using our proposed Biosecurity Risk Score (BRS), quantifying risk-associated effects while enabling scalable and safe ground-truth verification. Evaluations of 15 frontier models reveal substantial baseline threats: without tools, 67.6% of one-shot designs exceed the average measured variant, and 21.4% reach the highest-risk decile. Equipping models with BDTs significantly amplifies this risk, raising the pooled risk by (Delta BRS=+0.116) and the top-decile share to 23.5%. Code: https://github.com/drylab-bio/DRYLAB-Bench