An Early Warning of Emerging Biosecurity Risks in Frontier LLMs
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 biologica
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 52%Addressing benchmarking gaps in large language models for health and medicine with dynamic red-teaming - Nature →
- PossiblePossibly related (embedding) · 50%Our approach to bioresilience →
- PossiblePossibly related (embedding) · 50%What does "Safe AI" look like? [D] →
- PossiblePossibly related (embedding) · 49%Reproducibility in Computational Biology: Best Practices for AI and ML Workflows - Technology Networks →
- PossiblePossibly related (embedding) · 49%Best models for generating red-team attacks? Also looking for public datasets [R] →
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: zhou”