Can AI coding agents rebuild biological AI tools from a paper?
AISI's ReproBAIT evaluation finds coding agents can rebuild simple biological AI tools from limited public info within 4 hours and under $20, undermining withheld-code access controls.
Research
UK AI Security Institute
The UK government’s AI Security Institute: evaluations, red-teaming and research on frontier AI risks. aisi.gov.uk
Focus All
Topic Security & misuse + Evals
Source UK AISI · 11
Date Any time
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How Far Behind the Frontier are Leading Open Weight Models on Cyber?
AISI measures cyber capabilities of open and closed-weight models, finding leading open models now trail frontier closed models by 4-7 months, and discusses misuse risks of open release.
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Our evaluation of OpenAI's GPT-5.5 cyber capabilities
UK AISI reports cyber evaluations of GPT-5.5, finding it is the second model to complete their multi-step corporate network attack simulation end-to-end, suggesting a broader frontier trend.
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AISI Frontier AI Trends Report (2025)
UK AISI's first public report synthesising two years of frontier model testing, tracking trends in dangerous capabilities across national-security and public-safety domains for policymakers and the public.
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Our approach to tackling AI-generated child sexual abuse material
UK AISI and Thorn publish a safety protocol for AI developers to prevent AI-generated CSAM, alongside legislation allowing authorised testing of whether models can generate such material.
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Open technical problems in open-weight AI model risk management
Lists 16 open technical challenges for managing risks of open-weight frontier models across training data, algorithms, evaluations, deployment, and ecosystem monitoring, noting limited safety tooling.
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An example safety case for safeguards against misuse
Proposes an end-to-end safety case showing that misuse safeguards reduce AI assistant misuse risk to low levels, combining red-teaming effort estimates with a quantitative uplift model.
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Making safeguard evaluations actionable
Proposes how to make misuse-safeguard evaluations decision-relevant by setting metrics and thresholds, and presents an example safety case linking safeguard evaluation results to deployment decisions.
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Pre-Deployment evaluation of OpenAI’s o1 model
UK and US AI Safety Institutes jointly ran pre-deployment tests of OpenAI's o1 across cyber, biological, and AI-development capabilities, comparing it against reference models and sharing findings before release.
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Safety case template for frontier AI: A cyber inability argument
Proposes a safety case template using CAE structured arguments to show frontier AI models lack unacceptable offensive cyber capabilities, linking risk models, proxy tasks and evaluation results.
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Safety case template for ‘inability’ arguments
Proposes a safety case template for arguing that an AI system lacks offensive cyber capabilities, combining existing techniques as a proof of concept for frontier AI assurance.