# An Early Warning of Emerging Biosecurity Risks in Frontier LLMs

arXiv: https://arxiv.org/abs/2607.18056 · HTML: https://mlsanity.com/paper/2607.18056/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Jailbreaking, one of 12 directions in LLM safety and security.
- Builds on 28 of its 94 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Placed in

- LLM safety and security / [Jailbreaking](https://mlsanity.com/safety/jailbreaking/)

## Builds on (most cited first, 28 of 28)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [HybridFlow: A Flexible and Efficient RLHF Framework](https://mlsanity.com/paper/2409.19256/) (2024)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence](https://mlsanity.com/paper/2606.19348/) (2026)
- [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024)
- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [GLM-5: from Vibe Coding to Agentic Engineering](https://mlsanity.com/paper/2602.15763/) (2026)
- [Qwen3Guard Technical Report](https://mlsanity.com/paper/2510.14276/) (2025)
- [Intern-S1: A Scientific Multimodal Foundation Model](https://mlsanity.com/paper/2508.15763/) (2025)
- [Artificial intelligence and biological misuse: Differentiating risks of language models and biological design tools](https://mlsanity.com/paper/2306.13952/) (2023)
- [Can large language models democratize access to dual-use biotechnology?](https://mlsanity.com/paper/2306.03809/) (2023)
- [Jailbreak-R1: Exploring the Jailbreak Capabilities of LLMs via Reinforcement Learning](https://mlsanity.com/paper/2506.00782/) (2025)
- [MAGIC: A Co-Evolving Attacker-Defender Adversarial Game for Robust LLM Safety](https://mlsanity.com/paper/2602.01539/) (2026)
- [GeneBreaker: Jailbreak Attacks against DNA Language Models with Pathogenicity Guidance](https://mlsanity.com/paper/2505.23839/) (2025)
- [TRACE: Task-Aware Adaptive Self-Evolving Agentic Jailbreaking](https://mlsanity.com/paper/2605.30883/) (2026)
- [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)
- [EvoDefense: Co-Evolving Black-Box Defense with Large Language Models](https://mlsanity.com/paper/2605.31140/) (2026)
- [Towards AI-45circ Law: A Roadmap to Trustworthy AGI](https://mlsanity.com/paper/2412.14186/) (2024)
- [Not All Turns Matter: Credit Assignment for Multi-Turn Jailbreaking](https://mlsanity.com/paper/2605.08778/) (2026)
- [SafeSci: Safety Evaluation of Large Language Models in Science Domains and Beyond](https://mlsanity.com/paper/2603.01589/) (2026)

## Built on by (most cited first, 1 of 1)

- [JailbreakSkill: Scaling Automated Red-Teaming with Reusable and Ever-Evolving Skills](https://mlsanity.com/paper/2608.16465/) (2026)

## Abstract (arXiv)

Frontier large language models (LLMs) are increasingly integrated into scientific workflows, yet their growing biological capabilities may outpace current safeguards. To assess the biological risks of frontier models, we develop Intern-BioBreaker, a specialized bio-red-teaming model, together with an integrated computational-to-physical framework that couples model-level stress testing with wet-lab validation. Within this framework, Intern-BioBreaker generates targeted jailbreak prompts to test whether aligned models can be induced to provide operational guidance for safety-sensitive biological tasks or produce sequence-level outputs with potentially harmful properties. Selected sequence outputs are then carried forward for DNA synthesis, host expression, and orthogonal protein verification to assess whether model-generated designs can yield the intended biological products. Our evaluation reveals a concerning gap between text-level safeguards and the risks posed by capable scientific models: (i) Intern-BioBreaker outperforms baseline attack models and reveals widespread bio-risk jailbreak vulnerabilities across both open-weight and proprietary frontier LLMs, with several targets reaching near-saturated or 100% task-level attack success rate (ASR); (ii) in sequence-level case studies, GPT-5.5 can be induced to generate modified viral candidate sequences with pathogenic potential; the corresponding translated proteins may exhibit even stronger receptor-binding affinity and thus enhanced infection potential; and (iii) end-to-end verification shows that selected model-generated biological designs are not merely textual artifacts, but can be physically realized under controlled experimental settings. These findings underscore the need for stronger biological red-teaming, nucleic acid synthesis screening, and safety mechanisms that keep pace with model capabilities.