Prove what AI can do.
Misbar builds cryptographic infrastructure for confidential, verifiable AI evaluation. Test model performance and uncover vulnerabilities while keeping sensitive models, benchmarks, and findings private.
Trust the evidence, not just the claim
AI evaluations tend to require access to proprietary models, hidden test sets, sensitive prompts, or security findings.
Share everything, and you create confidentiality risk. Share too little, and nobody can verify the result independently.
Misbar is building a third option: verifiable evidence with controlled disclosure.
One pipeline, four stages
Commit
The model and the benchmark are each fixed to a digest before anything runs, so neither can change after the fact.
Evaluate
Scoring runs against the committed pair inside an enclave neither party can read.
Prove
The enclave emits a proof that the committed model was scored on the committed suite by the stated method.
Verify
Anyone holding the record can check the proof against the digests without seeing either input.
Two products. One evidence pipeline.
Private Benchmarking
Evaluate AI models against private tests. Verify defined aspects of the evaluation and scoring process without exposing the underlying assets.
Confidential Red-Teaming
Probe models for vulnerabilities and produce verifiable evidence of defined testing procedures and findings, while protecting sensitive attack materials.
Benchmarks measure performance against defined tests. Red-teaming searches for the failures nobody wrote a test for. Findings become regression tests. Benchmarks show whether the fix held.