Biosecurity
The Biosecurity Track supports research at the intersection of advanced AI and catastrophic biological risk. We are launching this track because the threat model has shifted. Biological foundation models, LLMs with growing wet-lab uplift, and AI-accelerated design tools are compressing timelines on capabilities that the existing biosecurity stack was not built to absorb. We want fellows pursuing technical work that has a realistic chance of meaningfully shifting outcomes within the next 6–12 months.
- Stage 1: Complete the general application, including track specific short-response questions
- Stage 2: Streams requiring empirical ML skills will include a standardized ML skills test. Other streams, which require more specific backgrounds, will skip directly to stream selection questions
- Stage 3: Interviews and work-tests
The track spans six research areas. Fellows are matched to mentors based on fit, and projects are scoped to produce concrete artifacts (e.g., papers, evals, prototypes, or policy analyses) by the end of the program.
- Detection
Metagenomic surveillance pipelines for pandemic-grade pathogen detection, genomic language models for novelty detection, and improved signal/noise at the front end of the surveillance stack. - Medical countermeasures
AI-accelerated discovery of antiviral peptides under pandemic-response constraints, paired with realistic analysis of the manufacturing and supply-chain bottlenecks that determine whether candidates actually reach patients. - AI for synthesis screening
Function-based DNA sequence screening using mechanistic interpretability and ML on biological foundation models (i.e., classifiers that catch hazardous sequences), including engineered and AI-designed variants meant to evade homology-based screens. - Physical biodefense
Engineering work on emergency biodefense infrastructure, such as PPE, filtration, far-UVC, decontamination, and improvised protective systems for worst-case scenarios. Deliverables here are often physical or quasi-physical. - Strategy and threat modeling
Policy and forecasting work on AI-bio, including evaluating policy levers, forecasting when AI trivializes specific offensive or defensive capabilities, and analyzing deterrence via physical chokepoints (e.g., synthesis screening governance, cloud-lab access controls). - Empirical AI × bio — defending against AI
Red-teaming biological AI models for dangerous capabilities, building technical defenses (e.g., genetic engineering attribution, data governance), and developing dangerous-capability evaluations for frontier AI-bio.
We expect fellows to engage seriously with infohazard considerations and to operate within a publication and disclosure framework that mentors will work through with fellows early on in the program. We anticipate that strong candidates will come from a variety of backgrounds, including biology, AI safety, public health, epidemiology, machine learning, engineering, chemistry, biosafety, biosecurity, and national security. If you're uncertain whether your background fits, apply anyway and tell us how you think about the threat model. Reasoning is more informative to us than credentials are.
In my stream, I focus on empirically measuring how frontier AI changes human capabilities in biology and the implications of it for biological risk. I’m particularly interested in real-world evaluations, understanding what drives AI-enabled uplift, and developing simulations or other proxies that can be validated against physical experiments and used to rapidly assess new models.
This stream is primarily focused on research into physical defenses against engineered pathogens, aiming to inform decisions about PPE stockpiling and distribution approaches, improve improvised PPE and bioshelter scale-up, and reach rapid conclusions on how much to prioritize other areas of physical biodefense (agriculture, emergency response, etc.). We are also open to strategic research into the use of bioweapons by AI or AI-human teams as part of takeover strategies and how this might inform preparedness.
This stream focuses on how advanced AI could enable new and dangerous bio technologies, and on assessing when risks become tractable or urgent as those capabilities arrive.
At Fourth Eon Biosecurity we're building adaptive, AI-native safeguards across the bioengineering stack, with a focus on function-based DNA synthesis screening. Fellows in this stream will work on technical research projects at the intersection of AI safety and biosecurity, aimed at reinforcing screening and generalizing detection beyond known threat signatures. Projects span mechanistic interpretability of bio foundation models, model evaluations for biosecurity-relevant capabilities, and agentic sequence analysis workflows.
本研究流将聚焦生物安全防护措施相关项目。
The stream focuses on evaluating and/or mitigating catastrophic risk emerging from dangerous scientific capabilities in frontier AI systems, with an emphasis on the challenges that emerge from lab integrations and novel science. Potential research directions include evaluation design, risk mitigations and evaluation science.
Computational/modelling problems in biosecurity.
Therapeutics may have durable advantages over pathogens even in the limit of technological progress. How can therapeutic development and manufacturing be made resilient under biorisk scenarios? How can AI progress be maximally leveraged for defense?