/analyzing-ransomware-payment-wallets
Traces ransomware cryptocurrency payment flows using blockchain analysis tools such as Chainalysis Reactor, WalletExplorer, and blockchain.com APIs, identifying wallet clusters and tracking fund movement through mixers and exchanges to support law enforcement attribution. Use
$ npx -y skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-ransomware-payment-wallets --agent claude-codeHow it fires
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- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
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/analyzing-ransomware-payment-wallets
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Traces ransomware cryptocurrency payment flows using blockchain analysis tools such as Chainalysis Reactor, WalletExplorer, and blockchain.com APIs, identifying wallet clusters and tracking fund movement through mixers and exchanges to support law enforcement attribution. Use
SKILL.md
analyzing-ransomware-payment-wallets.SKILL.mdname: analyzing-ransomware-payment-wallets
description: 'Traces ransomware cryptocurrency payment flows using blockchain analysis tools such as Chainalysis Reactor, WalletExplorer, and blockchain.com APIs, identifying wallet clusters and tracking fund movement through mixers and exchanges to support law enforcement attribution. Use when tracing ransomware bitcoin payments, performing cryptocurrency wallet forensics, or gathering blockchain threat intelligence on extortion payments.
'
domain: cybersecurity
subdomain: ransomware-defense
tags:
- ransomware
- blockchain
- cryptocurrency
- forensics
- threat-intelligence
- bitcoin
version: 1.0.0
author: mahipal
license: Apache-2.0
nist_csf:
- PR.DS-11
- RS.MA-01
- RC.RP-01
- PR.IR-01
mitre_attack:
- T1657
- T1486
mitre_f3:
version: '1.1'
tactics:
- monetization
- stealth
techniques:
- id: F1018
name: Convert to Cryptocurrency
tactic: monetization
source: f3
- id: F1017
name: Conversion to Physical Monetary Instruments
tactic: monetization
source: f3
- id: F1017.001
name: 'Conversion to Physical Monetary Instruments: Cash'
tactic: monetization
source: f3
- id: F1047
name: Transfer of funds
tactic: monetization
source: f3
- id: F1045
name: Structuring
tactic: stealth
source: f3Analyzing Ransomware Payment Wallets
When to Use
- An organization has been hit by ransomware and the ransom note contains a Bitcoin or cryptocurrency wallet address that needs investigation
- Law enforcement or incident responders need to trace where ransom payments flowed after the victim paid
- Threat intelligence analysts are attributing ransomware campaigns by clustering payment infrastructure across incidents
- Investigators need to determine if a ransomware group is reusing wallet infrastructure across multiple victims
- Compliance or legal teams need evidence of fund flows for prosecution, sanctions enforcement, or insurance claims
**Do not use** this skill for live payment interception or to interact directly with ransomware operators. All analysis should be passive and read-only against public blockchain data.
Prerequisites
- Python 3.8+ with `requests`, `json`, and `hashlib` libraries
- Access to blockchain explorer APIs (blockchain.com, WalletExplorer.com, Blockstream.info)
- Familiarity with Bitcoin transaction model (UTXOs, inputs, outputs, change addresses)
- Understanding of common obfuscation techniques (mixers, tumblers, peel chains, cross-chain swaps)
- Optional: Chainalysis Reactor license for enterprise-grade cluster analysis
- Optional: OXT.me for advanced transaction graph visualization
Workflow
Step 1: Extract Wallet Address from Ransom Note
Parse the ransom note to identify the payment address(es):
Common address formats:
Bitcoin (P2PKH): 1A1zP1eP5QGefi2DMPTfTL5SLmv7DivfNa (starts with 1)
Bitcoin (P2SH): 3J98t1WpEZ73CNmQviecrnyiWrnqRhWNLy (starts with 3)
Bitcoin (Bech32): bc1qar0srrr7xfkvy5l643lydnw9re59gtzzwf5mdq (starts with bc1)
Monero: 4... (95 characters, much harder to trace)
Ethereum: 0x... (40 hex chars)
Step 2: Query Blockchain Explorer for Transaction History
Retrieve all transactions associated with the wallet:
import requests
def get_wallet_transactions(address):
"""Query blockchain.com API for address transactions."""
url = f"https://blockchain.info/rawaddr/{address}"
resp = requests.get(url, timeout=30)
resp.raise_for_status()
data = resp.json()
return {
"address": address,
"n_tx": data.get("n_tx", 0),
"total_received_satoshi": data.get("total_received", 0),
"total_sent_satoshi": data.get("total_sent", 0),
"final_balance_satoshi": data.get("final_balance", 0),
"transactions": data.get("txs", []),
}Step 3: Map Fund Flow and Identify Clusters
Trace outputs from the ransom wallet to downstream addresses:
Fund Flow Analysis:
━━━━━━━━━━━━━━━━━━
Victim Payment ──► Ransom Wallet ──► Consolidation Wallet
├─► Mixer/Tumbler Service
├─► Exchange Deposit Address
└─► Peel Chain (sequential small outputs)
Key indicators:
- Consolidation: Multiple ransom payments aggregated into one wallet
- Peel chains: Sequential transactions with diminishing outputs
- Mixer usage: Funds sent to known mixer addresses (Wasabi, Samourai, ChipMixer)
- Exchange cashout: Deposits to known exchange wallets (Binance, Kraken hot wallets)Step 4: Cross-Reference with Known Wallet Databases
Check addresses against known ransomware infrastructure:
# Check WalletExplorer for entity identification
def check_wallet_explorer(address):
url = f"https://www.walletexplorer.com/api/1/address?address={address}&caller=research"
resp = requests.get(url, timeout=30)
data = resp.json()
return {
"wallet_id": data.get("wallet_id"),
"label": data.get("label", "Unknown"),
"is_exchange": data.get("is_exchange", False),
}Step 5: Generate Attribution Report
Compile findings into a structured intelligence report:
RANSOMWARE WALLET ANALYSIS REPORT
====================================
Ransom Address: bc1q...xyz
Family Attribution: LockBit 3.0 (based on ransom note format)
Total Received: 4.25 BTC ($178,500 at time of payment)
Total Sent: 4.25 BTC (wallet fully drained)
Number of Payments: 3 (likely 3 separate victims)
FUND FLOW:
Payment 1: 1.5 BTC → Consolidation wallet → Binance deposit
Payment 2: 1.0 BTC → Wasabi Mixer → Unknown
Payment 3: 1.75 BTC → Peel chain (12 hops) → OKX deposit
CLUSTER ANALYSIS:
Related wallets: 47 addresses identified in same cluster
Total cluster volume: 156.3 BTC ($6.5M USD)
First activity: 2024-01-15
Last activity: 2024-09-22
Verification
- Confirm wallet address for
Read more
name: analyzing-ransomware-payment-wallets
description: 'Traces ransomware cryptocurrency payment flows using blockchain analysis tools such as Chainalysis Reactor, WalletExplorer, and blockchain.com APIs, identifying wallet clusters and tracking fund movement through mixers and exchanges to support law enforcement attribution. Use when tracing ransomware bitcoin payments, performing cryptocurrency wallet forensics, or gathering blockchain threat intelligence on extortion payments.
'
domain: cybersecurity
subdomain: ransomware-defense
tags:
- ransomware
- blockchain
- cryptocurrency
- forensics
- threat-intelligence
- bitcoin
version: 1.0.0
author: mahipal
license: Apache-2.0
nist_csf:
- PR.DS-11
- RS.MA-01
- RC.RP-01
- PR.IR-01
mitre_attack:
- T1657
- T1486
mitre_f3:
version: '1.1'
tactics:
- monetization
- stealth
techniques:
- id: F1018
name: Convert to Cryptocurrency
tactic: monetization
source: f3
- id: F1017
name: Conversion to Physical Monetary Instruments
tactic: monetization
source: f3
- id: F1017.001
name: 'Conversion to Physical Monetary Instruments: Cash'
tactic: monetization
source: f3
- id: F1047
name: Transfer of funds
tactic: monetization
source: f3
- id: F1045
name: Structuring
tactic: stealth
source: f3Analyzing Ransomware Payment Wallets
When to Use
- An organization has been hit by ransomware and the ransom note contains a Bitcoin or cryptocurrency wallet address that needs investigation
- Law enforcement or incident responders need to trace where ransom payments flowed after the victim paid
- Threat intelligence analysts are attributing ransomware campaigns by clustering payment infrastructure across incidents
- Investigators need to determine if a ransomware group is reusing wallet infrastructure across multiple victims
- Compliance or legal teams need evidence of fund flows for prosecution, sanctions enforcement, or insurance claims
**Do not use** this skill for live payment interception or to interact directly with ransomware operators. All analysis should be passive and read-only against public blockchain data.
Prerequisites
- Python 3.8+ with `requests`, `json`, and `hashlib` libraries
- Access to blockchain explorer APIs (blockchain.com, WalletExplorer.com, Blockstream.info)
- Familiarity with Bitcoin transaction model (UTXOs, inputs, outputs, change addresses)
- Understanding of common obfuscation techniques (mixers, tumblers, peel chains, cross-chain swaps)
- Optional: Chainalysis Reactor license for enterprise-grade cluster analysis
- Optional: OXT.me for advanced transaction graph visualization
Workflow
Step 1: Extract Wallet Address from Ransom Note
Parse the ransom note to identify the payment address(es):
Common address formats: Bitcoin (P2PKH): 1A1zP1eP5QGefi2DMPTfTL5SLmv7DivfNa (starts with 1) Bitcoin (P2SH): 3J98t1WpEZ73CNmQviecrnyiWrnqRhWNLy (starts with 3) Bitcoin (Bech32): bc1qar0srrr7xfkvy5l643lydnw9re59gtzzwf5mdq (starts with bc1) Monero: 4... (95 characters, much harder to trace) Ethereum: 0x... (40 hex chars)
Step 2: Query Blockchain Explorer for Transaction History
Retrieve all transactions associated with the wallet:
import requests
def get_wallet_transactions(address):
"""Query blockchain.com API for address transactions."""
url = f"https://blockchain.info/rawaddr/{address}"
resp = requests.get(url, timeout=30)
resp.raise_for_status()
data = resp.json()
return {
"address": address,
"n_tx": data.get("n_tx", 0),
"total_received_satoshi": data.get("total_received", 0),
"total_sent_satoshi": data.get("total_sent", 0),
"final_balance_satoshi": data.get("final_balance", 0),
"transactions": data.get("txs", []),
}Step 3: Map Fund Flow and Identify Clusters
Trace outputs from the ransom wallet to downstream addresses:
Fund Flow Analysis:
━━━━━━━━━━━━━━━━━━
Victim Payment ──► Ransom Wallet ──► Consolidation Wallet
├─► Mixer/Tumbler Service
├─► Exchange Deposit Address
└─► Peel Chain (sequential small outputs)
Key indicators:
- Consolidation: Multiple ransom payments aggregated into one wallet
- Peel chains: Sequential transactions with diminishing outputs
- Mixer usage: Funds sent to known mixer addresses (Wasabi, Samourai, ChipMixer)
- Exchange cashout: Deposits to known exchange wallets (Binance, Kraken hot wallets)Step 4: Cross-Reference with Known Wallet Databases
Check addresses against known ransomware infrastructure:
# Check WalletExplorer for entity identification
def check_wallet_explorer(address):
url = f"https://www.walletexplorer.com/api/1/address?address={address}&caller=research"
resp = requests.get(url, timeout=30)
data = resp.json()
return {
"wallet_id": data.get("wallet_id"),
"label": data.get("label", "Unknown"),
"is_exchange": data.get("is_exchange", False),
}Step 5: Generate Attribution Report
Compile findings into a structured intelligence report:
RANSOMWARE WALLET ANALYSIS REPORT ==================================== Ransom Address: bc1q...xyz Family Attribution: LockBit 3.0 (based on ransom note format) Total Received: 4.25 BTC ($178,500 at time of payment) Total Sent: 4.25 BTC (wallet fully drained) Number of Payments: 3 (likely 3 separate victims) FUND FLOW: Payment 1: 1.5 BTC → Consolidation wallet → Binance deposit Payment 2: 1.0 BTC → Wasabi Mixer → Unknown Payment 3: 1.75 BTC → Peel chain (12 hops) → OKX deposit CLUSTER ANALYSIS: Related wallets: 47 addresses identified in same cluster Total cluster volume: 156.3 BTC ($6.5M USD) First activity: 2024-01-15 Last activity: 2024-09-22
Verification
- Confirm wallet address for
817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains · Apache 2.0
Repo: mukul975/Anthropic-Cybersecurity-Skills
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