Frontier AI labs are training systems that could pose international risks. Countries want agreements on safe development. Labs need ways to demonstrate compliance without exposing competitive advantages.

The technical infrastructure to make this possible doesn't exist yet. We have policy frameworks without verification systems. International agreements without monitoring tools. Compliance requirements without practical implementation paths.

This hackathon focuses on building that missing infrastructure. You'll have one intensive weekend to create verification protocols, monitoring tools, privacy-preserving compliance proofs, or coordination systems that could enable enforceable international cooperation on AI safety.

Top teams get:

Fast-tracks include at least one interview with leadership from Lucid Computing, or researchers at MIRI TGT or Apart Research.

What is International Technical AI Governance?

Technical international governance refers to the practical infrastructure needed to verify, monitor, and enforce international agreements on AI development. This includes:

• Hardware verification that tracks compute resources used in training frontier models

• Attestation systems that cryptographically prove model properties without revealing weights

• Privacy-preserving compliance proofs using zero-knowledge cryptography or trusted execution environments

• Risk threshold frameworks that define when capabilities trigger safety requirements

• International coordination mechanisms that enable verification between parties without full trust

• Dual-use detection for identifying dangerous capabilities in AI research before deployment

Labs are training increasingly capable systems. Some pose risks that cross borders. International cooperation requires technical mechanisms to verify compliance without exposing sensitive information or creating security vulnerabilities.

Why this hackathon?

The Problem

AI systems get more capable, our governance infrastructure doesn't. Multiple labs are training models above the EU's 10²⁵ FLOP threshold for systemic risk. Some frontier models now require ASL-3 safeguards. Decentralized training makes compute monitoring harder to implement.

The EU AI Act took effect in August 2024, but practical compliance tools remain scarce. Export controls on AI chips lack verification mechanisms. Responsible scaling policies define thresholds without automated monitoring. Labs coordinate through voluntary frameworks that lack enforcement infrastructure.

Most governance proposals assume technical capabilities that don't exist yet. They require compute tracking systems not deployed at scale, attestation mechanisms not integrated into hardware, and verification protocols not tested between adversarial parties. We're building policy without infrastructure.

Why International Technical AI Governance Matters Now

International cooperation depends on verifiable compliance. If agreements can't be verified without exposing sensitive IP or creating security risks, countries won't sign them. Labs won't share information that compromises their competitive position.

We're massively under-investing in governance infrastructure. Most effort goes into capabilities research or post-deployment harm mitigation. Far less into building the verification systems, monitoring tools, and coordination mechanisms that enable international agreements.

Better technical infrastructure could give us agreements that labs can verify without exposing model weights, monitoring systems that respect privacy while enabling compliance checks, and coordination mechanisms that work between parties without full trust. It could create the practical foundation needed for international cooperation on frontier AI safety.

Hackathon Tracks

1. Hardware Verification & Attestation

• Design hardware verification protocols for tracking compute resources in datacenter environments

• Build attestation systems using trusted execution environments (TEEs) that prove model properties without exposing weights

• Create compute monitoring tools that detect training runs above regulatory thresholds

• Develop chip-level security mechanisms for remote verification of AI hardware properties

2. Compliance Infrastructure & Privacy-Preserving Proofs

• Build zero-knowledge proof systems that demonstrate regulatory compliance without revealing sensitive information

• Create privacy-preserving audit mechanisms for federated learning or distributed training

• Develop compliance automation tools for EU AI Act requirements, GPAI reporting, or safety frameworks

• Design cryptographic protocols that enable verification between parties without full trust

3. Risk Thresholds & Compute Verification

• Build risk assessment frameworks that map compute thresholds to capability levels

• Create tools for harmonizing ASL/CCL terminology across different lab safety frameworks

• Develop capability evaluation systems for dual-use risks (CBRN, cyber, autonomous AI R&D)

• Design monitoring systems for responsible scaling policies and deployment safeguards

4. International Verification & Coordination

• Build coordination infrastructure for International Network of AI Safety Institutes

• Create verification mechanisms inspired by IAEA frameworks adapted for AI governance

• Develop systems for cross-border information sharing that respect national security concerns

• Design tools for implementing global AI safety standards and red lines

5. Research Governance & Dual-Use Detection

• Build detection systems for identifying dangerous capabilities in pre-publication research

• Create frameworks for assessing dual-use risks in biological AI models or other specialized domains

• Develop pre-publication review tools that scale across research communities

• Design capability-based threat assessment systems for frontier AI research

Who should participate?

This hackathon is for people who want to build solutions to technological risk using technology itself.

You should participate if you're an engineer, researcher, or developer who wants to work on consequential problems and build practical verification, monitoring, or compliance infrastructure.

No prior governance research experience required. We provide resources, mentors, and starter templates.

What you will do

Participants will:

• Form teams or join existing groups.

• Develop projects over an intensive hackathon weekend.

• Submit open-source verification tools, compliance systems, monitoring infrastructure, or empirical research advancing international AI governance

Please note: Due to the high volume of submissions, we cannot guarantee written feedback for every participant, although all projects will be evaluated.

What happens next

Winning and promising projects will be:

• Awarded $2,000 in cash prizes

• Fast-tracked for interviews with Lucid Computing, MIRI TGT, or Apart Research

• Published openly for the community

• Invited to continue development within the Apart Fellowship

• Shared with relevant safety researchers and policymakers.

Why join?

• Work on consequential problems: Build infrastructure that could enable international cooperation on frontier AI safety

• Learn from experts: Get guidance from AI safety researchers and technical governance practitioners throughout the weekend

• Build your network: Collaborate with technical talent from across the globe focused on AI safety

• Develop practical skills: Gain hands-on experience with verification systems, cryptographic proofs, or monitoring infrastructure that employers value