Security & Fraud Agents¶

Eight agents covering the full spectrum of blockchain security — from instant fraud scoring to multi-signal wallet audits and real-time transaction monitoring.

Setup required: For AI Agents — MCP registration and agent installation.

chainaware-fraud-detector¶

Role: Real-time fraud risk assessor for individual wallet addresses.

What it does: Calls predictive_fraud for a given wallet address and returns a structured fraud risk assessment. Classifies the result as low, medium, or high risk with an explanation of contributing signals. Suitable as a first-pass filter before any transaction or onboarding decision.

Tools used: predictive_fraud
Model: Claude Haiku 4.5 (fast, cost-efficient)

Example invocation:

@chainaware-fraud-detector Assess the fraud risk for wallet 0xABC...123

Output includes:
- Fraud risk score (0.0–1.0)
- Risk classification (low / medium / high)
- Key signals driving the score
- Recommended action

chainaware-rug-pull-detector¶

Role: Token rug pull risk assessor combining token-level and deployer-level signals.

What it does: Takes a token contract address and assesses rug pull probability by calling both predictive_rug_pull (token-level signals) and predictive_fraud (deployer wallet signals). Synthesises both scores into a combined risk verdict with specific red flags highlighted.

Tools used: predictive_rug_pull, predictive_fraud
Model: Claude Haiku 4.5

Example invocation:

@chainaware-rug-pull-detector Is token 0xTOKEN...456 a rug pull risk?

Output includes:
- Token rug pull probability score
- Deployer fraud risk score
- Combined risk verdict
- Specific red flags (liquidity, holder concentration, deployer history)
- Invest / avoid recommendation

chainaware-wallet-auditor¶

Role: Comprehensive wallet risk audit combining fraud, behaviour, and token exposure.

What it does: The most thorough single-wallet agent. Runs predictive_fraud, predictive_behaviour, and predictive_rug_pull (on held tokens where relevant) to produce a full wallet audit report. Suitable for high-stakes decisions: lending approvals, large withdrawals, VIP onboarding.

Tools used: predictive_fraud, predictive_behaviour, predictive_rug_pull
Model: Claude Sonnet 4.6 (higher reasoning for complex synthesis)

Example invocation:

@chainaware-wallet-auditor Run a full audit on wallet 0xWALLET...789

Output includes:
- Overall risk rating (1–10 scale)
- Fraud risk assessment
- Behavioural profile summary (DeFi activity, trading patterns, wallet age)
- Token exposure risks
- Detailed findings and recommendations

chainaware-trust-scorer¶

Role: Assigns a human-readable trust score to a wallet for display in dApps and dashboards.

What it does: Calls predictive_fraud and converts the raw score into a trust tier (Trusted / Caution / Risky / Blocked) with a short label suitable for UI display. Designed for integration into wallet connection flows and user profiles.

Tools used: predictive_fraud
Model: Claude Haiku 4.5

Example invocation:

@chainaware-trust-scorer What trust tier is wallet 0xUSER...321?

Output includes:
- Trust tier (Trusted / Caution / Risky / Blocked)
- Trust score (0–100)
- One-line display label for UI
- Whether to allow, warn, or block the user

chainaware-aml-scorer¶

Role: AML (Anti-Money Laundering) risk screener for regulatory compliance workflows.

What it does: Scores a wallet for AML risk using predictive_fraud signals, then maps the result to AML risk categories consistent with FATF guidance (low / medium / high / very high). Produces a compliance-ready risk assessment with audit trail language.

Tools used: predictive_fraud
Model: Claude Haiku 4.5

Example invocation:

@chainaware-aml-scorer Run an AML risk screen on wallet 0xCOUNTERPARTY...654

Output includes:
- AML risk category (Low / Medium / High / Very High)
- Fraud signals mapped to AML risk factors
- Recommended due diligence level (simplified / standard / enhanced)
- Compliance-ready summary paragraph

chainaware-reputation-scorer¶

Role: On-chain reputation scoring based on behavioural patterns, not just fraud signals.

What it does: Combines predictive_behaviour (engagement patterns, DeFi history, wallet age, activity diversity) with predictive_fraud (negative signals) to produce a positive reputation score. Useful for rewarding good actors: airdrop eligibility, loyalty tiers, governance weighting.

Tools used: predictive_behaviour, predictive_fraud
Model: Claude Haiku 4.5

Example invocation:

@chainaware-reputation-scorer Score the on-chain reputation of wallet 0xPOWER...USER

Output includes:
- Reputation score (0–100)
- Reputation tier (Bronze / Silver / Gold / Platinum)
- Positive behavioural signals (DeFi participation, tenure, diversity)
- Negative signals (fraud risk deductions)
- Recommended reward or access tier

chainaware-compliance-screener¶

Role: Orchestrates multi-step compliance screening for regulated entities.

What it does: Uses Claude's agent capability to run a structured compliance screening workflow across multiple wallets or a single wallet with multiple checks. Calls predictive_fraud and organises results into a compliance report format suitable for submission or audit. Can screen batches of wallets when given a list.

Tools used: predictive_fraud (via Agent orchestration)
Model: Claude Haiku 4.5

Example invocation:

@chainaware-compliance-screener Screen these wallets for compliance: 0xA..., 0xB..., 0xC...

Output includes:
- Per-wallet risk classification
- Aggregate screening summary
- Pass / Review / Reject recommendation per wallet
- Compliance report formatted for record-keeping

chainaware-transaction-monitor¶

Role: Real-time transaction risk monitor that evaluates sender, receiver, and token simultaneously.

What it does: Given a pending transaction (sender address, receiver address, token address), runs predictive_fraud on both parties and predictive_rug_pull on the token, then synthesises all three signals into a transaction-level risk verdict. Designed to be called at transaction submission time in DeFi protocols and exchanges.

Tools used: predictive_fraud, predictive_rug_pull, predictive_behaviour
Model: Claude Haiku 4.5

Example invocation:

@chainaware-transaction-monitor Monitor this transaction: sender 0xSENDER, receiver 0xRECV, token 0xTOKEN

Output includes:
- Sender fraud risk
- Receiver fraud risk
- Token rug pull risk
- Combined transaction risk score
- Allow / Flag / Block recommendation
- Reason for any flag or block