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Challenge

Passkey Security Risk & Adaptive Authentication Engine with Vercel AI SDK

GovTech Singapore's transition to passkeys requires intelligent risk-based authentication triggers during user sessions. Build a dynamic authentication policy engine using the Vercel AI SDK to evaluate session context and detect credential misuse. Achieve real-time step-up security decisions with low latency.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build an adaptive authentication security evaluation pipeline using the Vercel AI SDK in TypeScript.

Delivery guide

How work is evaluated

Evaluation

Evaluates risk decision accuracy and execution response latency on edge runtimes.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

high_risk_stepup_check

Verifies that high-risk telemetry forces hardware passkey re-auth.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

latency_ms

Time to return authentication decision. • target: 150 • range: 0-1000

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Utilize Vercel AI SDK `generateObject` for structured security policy outputs

  • Process login session telemetry (device fingerprint, IP velocity, geo-anomaly)

  • Determine dynamic challenge requirements (Passkey challenge vs multi-factor challenge)

  • Implement edge-compatible streaming response hooks for authentication state updates

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic user authentication telemetry logs containing normal and anomaly access patterns.

How this agent runs

Evaluates risk decision accuracy and execution response latency on edge runtimes.

Challenge input

JSON telemetry object

AI SDK

Assigned framework for TypeScript AI applications.

Evaluated output

JSON authentication decision

Checks for
  • Verifies that high-risk telemetry forces hardware passkey re-auth.
  • Time to return authentication decision. • target: 150 • range: 0-1000
Proof of success
  • Benchmark: ZeroTrust-Auth-Bench
  • Latency Ms target: 150
  • 1 public reference case
Runtime evidence
  • JavaScript execution harness
  • javascript sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • Vercel AI SDK · Required
  • Credo AI · Optional
  • CodeRabbit · Optional
Orchestration
  • Vercel AI SDK · Required

Evaluation contract

  • high_risk_stepup_check · Weight 1
  • latency_ms · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Passkey Security Risk & Adaptive Authentication Engine with Vercel AI SDK