neuron-tai/.scratch/alpha-hardening/issues/09-fraud-reputation-routing-adaptive-audit.md

Status: ready-for-agent

09 — FRAUD: Reputation-weighted routing + adaptive audit rate

What to build

Wire reputation into route selection and audit sampling. Default network audit budget ≈5% — not a cap. New/low-reputation nodes: 20–30% audit rate; veterans: 2–3% floor ≥2%. Tripwires escalate rate without direct punishment.

Code refs:

• packages/tracker/meshnet_tracker/server.py — route selection _select_route, _effective_throughput (~1747, routing helpers)
• packages/validator/meshnet_validator/__init__.py — sample_rate=0.05
• Research: .scratch/alpha-hardening/research-verifiable-inference.md §1.1, §6, §8 layers 2–4

Audit selection must be unpredictable at request time (tracker RNG after commitment window opens).

Test-first

1. Red: uniform 5% sample regardless of reputation — test expects higher rate for low-reputation wallet.
2. Green: budget balancer keeps fleet-wide average ≈ configured target.
3. Routing prefers higher reputation among equal throughput candidates.

Acceptance criteria

• Per-wallet audit probability function of reputation (newcomer high, veteran low, floor ≥2%)
• Fleet-wide audit budget configurable (~5% default target); over ≥1000 requests with fixed seed, measured fleet audit rate within ±1.0 percentage point of configured target (e.g. 4.0–6.0% at 5% default)
• Route scoring includes reputation multiplier (earnings scale with tenure)
• Passive tripwire flags (perplexity/repetition) bump audit rate only
• Tests: deterministic seed for sampling distribution checks

ADR links

• ADR-0018 §1, §6–7
• ADR-0013

Blocked by

• 08-fraud-reputation-model-persistence.md