AI Verification Grand Challenge
In two stages: remote-first, with progression for selected semi-finalists in Stage 2 to an in-person, all-expenses-paid trip to Singapore.
Registration open
Closes Jan 15, 2027.
- Jan–Feb 2027
- Online stage, 4 weeks
- Mar 2027
- Finals in Singapore, 3 days
Overview
AI is getting more powerful very quickly, and some of the risks could be catastrophic. Companies are racing each other, so none of them can easily slow down alone.
One way out is for the US and China to agree to pace how fast the most advanced AI systems are built. The hard part is trust. Each side needs proof that the other is keeping its word, without handing over its secrets.
AI verification is the technology that makes that proof possible. It means tools that can check what an AI data center is actually doing, so a deal can be enforced without anyone giving up their intellectual property.
Right now, those tools don’t exist in a form both sides would trust. That makes this a technical problem more than a diplomatic one. Until it’s solved, an international agreement on AI can’t really be enforced.
Key dates
- October–November 2026: Register your interest
- December 2026: Projects launched
- January–February 2027: Phase 1 online sprint (4 weeks). Teams of up to five tackle five focused verification problems and are scored on live leaderboards.
- March 2027: Phase 2 finals in Singapore (3 days). The top 25 participants build working prototypes in person. Verification experts try to break the prototypes and judge the results.
Program overview
- Duration: 4 weeks online + long weekend in person
- Locations: Remote · Singapore
- Format: Two challenge stages · Online → In person
- Support: Compute · APIs · Community · In-person networking + travel · Prizes
Why join
- Win prizes. Top performers earn cash prizes, and the finalists’ work is recognized publicly.
- Shape the field, not just a leaderboard. Your findings go straight into the research. They inform future challenge cycles and become the foundation for the Grand Challenges that follow.
- Help set the standard. Participants get elevated governance rights, with a real say in how benchmarks are designed and iterated going forward.
- Follow a clear path from newcomer to contributor. Start online with access to the community. Climb the public leaderboard and become a finalist. Then meet people in person in Singapore, and get the chance to contribute to or co-author the research paper.
- Get recognized for what you build. Both the online and in-person stages end with an awards ceremony and a publicly visible leaderboard. You leave with concrete, verifiable wins for your portfolio.
- Meet the people working on this. The in-person finals put you in the same room as verification experts and other top researchers.
- Open doors to work. Strong performers may get direct hiring opportunities, through our partners and community and through the wider network of organizations involved.
Ready to build rigorous foundations for trustworthy AI?
Registration opens 15th October → 15th January (23:59 UTC). Team slots will be filled on a rolling basis.
Focus areas
Please note, tracks are subject to change and will be confirmed ahead of challenge launch.
| Adversarial Workload Classification | Disguise an AI training run as ordinary inference to fool a monitor that watches GPU activity. |
| Verified Inference Beyond Dense Models | Try to fool a cryptographic check into thinking an unapproved AI model is actually an approved one. |
| Fake an Evaluation Attestation | Pose as a dishonest model provider and forge the signed record that a safety evaluation ran on a model inside a secure hardware enclave, across three levels of defense. |
| Hide Compute from a Proof-of-Work Check | Sneak an unapproved workload onto GPUs while still passing a verifier that checks only the approved work is running. |
| Sabotage an Inspector Agent | Act as a facility operator and either fool an AI inspector with doctored evidence or trick it into leaking the confidential data it saw. |
Who should join
Security researchers and red-teamers, cryptographers, ML and GPU engineers, hardware and confidential-computing specialists, and AI agent builders, plus strong students and early-career researchers from any of these fields.
You don’t need an AI safety background. If you like breaking systems or proving they can’t be broken, there’s a challenge for you. The challenges span a wide range of difficulty and specialisms, so there’s an entry point whether you’re new to the field or a specialist.
Teams of up to five are encouraged, so bring complementary skills.
Tip: The best teams will mix one or two people who know the technical area with someone who thinks like an attacker.
Useful skill sets
- GPU performance engineering and ML infrastructure (CUDA, profiling, distributed training, LLM inference serving)
- Adversarial ML, including evading classifiers and monitors
- Applied cryptography (zero-knowledge proofs, verifiable computation, proof systems)
- Mixture-of-experts and other non-dense model architectures
- Confidential computing and hardware security (trusted execution environments, remote attestation, firmware)
- Offensive security, red-teaming and CTF experience, including cloud security
- LLM agent building and LLM red-teaming (prompt injection, jailbreaks, agent tooling)
- Theoretical computer science or blockchain protocol research
FAQ
Do I need an AI safety background?
No: strong expertise in formal methods, cryptography, or interpretability is what we value most. We’ll help you connect that expertise to AI safety problems.
What is the application process?
Fill in the application form. Candidates can choose their own challenge track. You can also choose to compete in more than one challenge track.
Full challenge rules coming soon.