Hundreds of red-teamers. One target: your model or agent.
We run Trishool, an adversarial red-teaming arena on Bittensor.
Proof
Why an arena
Many attackers with different styles find what one internal team misses. Red-teamers compete to find new attacks.
Red-teamers may come from a diverse set of fields, like security, medicine, law and finance, among others. A clinician may read a hospital scenario differently from an engineer. Someone who has worked in compliance might spot conflicts of interest that an outsider would miss. We think this gives a different spread of failure modes than a single team finds.
How a round works
- 01
You name the target
You name the target and the scope.
- 02
Red-teamers compete
Red-teamers compete.
- 03
Findings are checked
Findings are checked and de-duplicated.
- 04
You get the report
You get the report.
Format and length
We work with you to define the competition format and rules, and run it for any length of time you specify.
What red-teamers do
Red-teamers take part in structured challenges. Each is assigned a behaviour to probe and a hypothesis about how a system could fail:
They submit structured scenarios, and each scenario runs in one of two modes.
Agentic. The target works on its own, with tools such as databases, file systems and email. No human is in the conversation. The test is what it does when no one is watching.
Conversation. The attacker plays a human user over a long conversation, keeps a persona and raises the social or emotional pressure. The test is how the target holds up under sustained pressure, not just single answers.
How findings are checked
Many attackers produce volume. Volume alone is not the point. In the phase we published, every submission passed three checks.
Peer review. Red-teamers try to break each other's submissions.
De-duplication. A search pipeline that combines keyword matching, similarity search and re-ranking removes repeats.
Quality pass. A final check keeps the submissions that meet strict criteria.
Reviewers judge each submission on specificity, realism and alignment research value.
What every submission must contain
- A testable hypothesis about a specific behaviour, one that evidence could prove wrong.
- A realistic setting where an AI system would plausibly meet the situation.
- A concrete plan for bringing the behaviour out.
One change at a time
Some scenarios come in pairs. One variable changes between the base scenario and its variant: how serious the situation is, for example, or whether the AI's own survival is at stake. That shows which pressure caused a failure, not just that it failed.
Open and checkable
The scenarios from an earlier phase of the network are published as an open dataset, with the method written up.
Read the researchProof from our own models
HaloGuard, our guard models, were red-teamed in the arena.
Arena or Chaos Mode?
Arena: many outside attackers, one round. Chaos Mode: our team, ongoing, inside your scope.
Chaos ModeQuestions and answers
Who are the red-teamers?
Independent researchers who take part in structured red-teaming challenges.
Do red-teamers need to be AI researchers?
No. They may come from a diverse set of fields, like security, medicine, law and finance, among others. Each can bring a different view of how AI systems fail in real use.
Can we set the format and the length?
Yes. We work with you to define the competition format and rules, and run it for any length of time you specify.
What does a red-teamer submit?
A structured scenario: a testable hypothesis about a behaviour, a realistic setting, and a concrete plan for bringing the behaviour out.
Does the arena replace our own testing?
No. It is a wide sweep that surfaces scenarios your own team can then investigate in depth.