Skip to content
cccskills
FreePublished on GitHub

analyzing-ransomware-payment-wallets

Investigate ransomware payment addresses using public blockchain records. Trace transfers, group addresses likely controlled by the same entity, identify links to exchanges or mixing services, and organize findings into a report for incident responders and investigators.

OriginalShow the original 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.

Works withBinance
See how to install

Good for

  • Investigating a payment address found in a ransom note
  • Tracing funds after a ransom payment
  • Comparing related wallets across ransomware incidents
  • Preparing evidence of cryptocurrency fund flows

Files included (4)

  • SKILL.md7.6 KB
  • LICENSE11.0 KB
  • references/api-reference.md3.0 KB
  • scripts/agent.py7.8 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Analyzing 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 format is valid before querying APIs
  • Cross-reference transaction timestamps with known incident timelines
  • Validate cluster associations by checking common-input-ownership heuristic
  • Compare findings against OFAC SDN list for sanctioned addresses
  • Verify exchange attribution against multiple sources (WalletExplorer, OXT, Chainalysis)

Key Concepts

TermDefinition
UTXOUnspent Transaction Output; the fundamental unit of Bitcoin that tracks ownership through a chain of transactions
Cluster AnalysisGrouping multiple Bitcoin addresses believed to be controlled by the same entity using common-input-ownership and change-address heuristics
Peel ChainA laundering pattern where funds are sent through many sequential transactions, each peeling off a small amount to a new address
CoinJoin/MixerPrivacy techniques that combine multiple users' transactions to obscure the link between sender and receiver
Common Input OwnershipHeuristic that assumes all inputs to a single transaction are controlled by the same entity

Tools & Systems

  • Chainalysis Reactor: Enterprise blockchain investigation platform with entity attribution and cross-chain tracing
  • WalletExplorer: Free tool that clusters Bitcoin addresses and labels known services (exchanges, mixers, markets)
  • OXT.me: Advanced Bitcoin transaction visualization with UTXO graph analysis
  • Blockstream.info: Open-source Bitcoin block explorer with full API access
  • blockchain.com API: Free API for querying Bitcoin address balances and transaction histories
  • OFAC SDN List: U.S. Treasury sanctioned address list for compliance checking

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Assess exposure of Windows secrets protected by DPAPI, the operating system’s data protection service, during authorized security tests. Guide examination and decryption of saved credentials and browser data using available user sessions, credentials, or backup keys.

  • Assessing saved credential exposure in authorized tests
  • Examining collected DPAPI files offline
  • Reviewing protected browser and RDP credentials
mukul975/Anthropic-Cybersecurity-Skills34.7KUpdated Aug 31, 2026

Assess Shadow Credentials account takeover during authorized Active Directory testing. Examine permissions that allow public key registration, validate authentication without a password reset, and document credential cleanup, detection opportunities, and access control changes needed for remediation.

  • Assessing risky permissions identified in BloodHound
  • Validating account takeover without resetting a password
  • Generating directory change records during security exercises
mukul975/Anthropic-Cybersecurity-Skills34.7KUpdated Aug 31, 2026

Prepare for CMMC Level 2, a cybersecurity certification for defense contractors. Define the systems handling controlled information, review security requirements, calculate the SPRS assessment score, and organize remediation plans and evidence into a readiness report.

  • Define the assessment scope for systems handling controlled information
  • Calculate an SPRS score and prioritize unmet security requirements
  • Prepare security plans, remediation milestones, and assessment evidence
mukul975/Anthropic-Cybersecurity-Skills34.7KUpdated Aug 31, 2026

Create disk images for investigations using dd or dcfldd on Linux. The workflow covers write protection, bit-for-bit copying, hash checks to verify integrity, and documentation of the acquisition process.

  • Preserving a suspect drive before investigation
  • Creating evidence copies of USB devices or memory cards
  • Verifying that a disk image matches its source
mukul975/Anthropic-Cybersecurity-Skills34.7KUpdated Aug 31, 2026

Review Active Directory permissions with ldap3, a Python library for directory queries. Identify excessive access granted to non-administrative accounts, explain potential privilege escalation paths, and produce a JSON report with affected objects and remediation recommendations.

  • Auditing excessive Active Directory permissions
  • Investigating privilege escalation paths from ordinary accounts
  • Reporting affected objects and permission remediation recommendations
mukul975/Anthropic-Cybersecurity-Skills34.7KUpdated Aug 31, 2026

Inspect suspicious Android APK files without running them. Analyze permissions, app components, suspicious code patterns, and network indicators, then produce a JSON risk report to support malware triage and detection rule development.

  • Triage suspicious APK files without running them
  • Inspect dangerous permission combinations and suspicious API calls
  • Extract network indicators for malware detection rules
mukul975/Anthropic-Cybersecurity-Skills34.7KUpdated Aug 31, 2026

See all skills by mukul975

Report a problem with this skill