Kimi K2.6 vs GPT-5.5 in Authorization Testing
What two real authorization traces showed us about persistence, restraint, and the evidence gates Tahr needs around both.
Insight: Model choice changes how hard an agent pushes before evidence promotion.
What two real authorization traces showed us about persistence, restraint, and the evidence gates Tahr needs around both.
Insight: Model choice changes how hard an agent pushes before evidence promotion.
Tahr treats access control as a coverage problem across identities, operations, object owners, and tenants, then promotes only proof-backed violations.
Insight: Access control testing should be modeled as coverage across users, tenants, objects, and operations.
Most AI security systems constrain every attack agent. We take a different approach: let some agents explore widely, then rely on a downstream verifier to keep only what is real.
Insight: Exploration and verification work better when different agents own each job.
We pair every AI testing agent with a second agent that independently re-exploits each finding using statistical verification and drops anything it can't reproduce.
Insight: AI findings need independent re-exploitation before they reach a report.