The FAR AI security runtime operates on four pillars:Documentation Index
Fetch the complete documentation index at: https://wp.farlabs.ai/llms.txt
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- Model integrity - when a node commits to serving a specific model, it also commits to a cryptographic fingerprint of that model’s weights. The orchestrator continuously issues random challenges that the node can only answer correctly if it is genuinely holding and running the committed model. A node serving a different model cannot pass these challenges.
- Hardware presence - specialized proof-of-work challenges verify that the node’s declared GPU is genuinely present, active, and holding the committed model in memory.These challenges have strict timing requirements that cannot be met by a node fetching data from a remote source on demand.
- Network topology - the orchestrator verifies that a node’s declared network configuration is consistent with its observed latency and connectivity patterns, making it impossible to misrepresent hardware location or connectivity.
- Settlement integrity - every completed job generates a tamper-evident, cryptographically chained record linking the job to the node, the model, and the energy consumed. The orchestrator reconciles these records against its own independent history. Any gap, replay, or discrepancy is detected automatically.
Every node is verified continuously, not just at onboarding, but throughout every job it runs.