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分析勒索軟體付款錢包

使用 Chainalysis Reactor、WalletExplorer 和 blockchain.com API 等區塊鏈分析工具,追蹤勒索軟體加密貨幣的付款流向,識別錢包集群,並追蹤資金透過混幣器和交易所的流動,以協助執法機關歸因調查。

分析勒索軟體付款錢包 是什麼?

這個技能適用於需要分析勒索軟體付款地址的場景,例如當組織遭受勒索軟體攻擊、支付贖金後,或威脅情報分析師需要關聯不同事件的付款基礎設施時。它透過被動查詢公開的區塊鏈資料,解析贖金筆記中的錢包地址,並利用區塊鏈瀏覽器 API 獲取交易歷史。技能會自動識別常見的混淆模式,如合併地址、剝離鏈(Peel Chain)和混幣器使用,並透過 WalletExplorer 等工具進行已知實體識別。最終產出結構化的歸因報告,詳細記錄資金流向、集群分析和潛在的交易所兌現點。此技能支援多種錢包格式(Bitcoin P2PKH、P2SH、Bech32、Monero、Ethereum),並可與 Chainalysis Reactor 等企業級分析平台整合,以進行更深入的調查。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-ransomware-payment-wallets

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說明文件

分析勒索軟體付款錢包 是做什麼的?

使用時機

  • 組織遭受勒索軟體攻擊,且贖金筆記中包含需要調查的 Bitcoin 或加密貨幣錢包地址
  • 執法機關或事件應變人員需要追蹤受害者付款後,贖金流向何處
  • 威脅情報分析師正在透過關聯各事件的付款基礎設施,來歸因勒索軟體攻擊活動
  • 調查人員需要判斷某勒索軟體組織是否在多個受害者之間重複使用錢包基礎設施
  • 合規或法務團隊需要資金流向證據,以用於起訴、制裁執行或保險理賠

請勿使用此技能進行即時付款攔截或直接與勒索軟體操作者互動。所有分析應為被動且唯讀狀態,僅針對公開的區塊鏈資料。

前置條件

  • Python 3.8+,並安裝 requests、json 和 hashlib 函式庫
  • 可存取區塊瀏覽器 API(blockchain.com、WalletExplorer.com、Blockstream.info)
  • 熟悉 Bitcoin 交易模型(UTXO、輸入、輸出、找零地址)
  • 了解常見的混淆技術(混幣器、翻攪器、剝離鏈、跨鏈交換)
  • 選用:Chainalysis Reactor 授權,以進行企業級集群分析
  • 選用:OXT.me,以進行進階交易圖表視覺化

工作流程

步驟 1:從贖金筆記中提取錢包地址

解析贖金筆記,識別出付款地址:

常見地址格式:
  Bitcoin (P2PKH):   1A1zP1eP5QGefi2DMPTfTL5SLmv7DivfNa  (開頭為 1)
  Bitcoin (P2SH):    3J98t1WpEZ73CNmQviecrnyiWrnqRhWNLy  (開頭為 3)
  Bitcoin (Bech32):  bc1qar0srrr7xfkvy5l643lydnw9re59gtzzwf5mdq (開頭為 bc1)
  Monero:            4... (95 個字元,更難追蹤)
  Ethereum:          0x... (40 個十六進制字元)

步驟 2:查詢區塊瀏覽器以獲取交易歷史

檢索與該錢包相關的所有交易:

import requests

def get_wallet_transactions(address):
    """透過 blockchain.com API 查詢地址的交易記錄。"""
    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", []),
    }

步驟 3:繪製資金流向並識別集群

從勒索錢包追蹤輸出至下游地址:

資金流向分析:
━━━━━━━━━━━━━━━━━━
受害者付款 ──► 勒索錢包 ──► 合併錢包
                                  ├─► 混幣器/翻攪服務
                                  ├─► 交易所存款地址
                                  └─► 剝離鏈 (連續的小額輸出)

關鍵指標:
  - 合併:多筆勒索付款彙整到一個錢包
  - 剝離鏈:連續交易且輸出金額遞減
  - 混幣器使用:資金發送至已知的混幣器地址(Wasabi、Samourai、ChipMixer)
  - 交易所兌現:存入已知的交易所錢包(Binance、Kraken 熱錢包)

步驟 4:與已知錢包資料庫交叉比對

檢查地址是否與已知的勒索軟體基礎設施相關:

# 透過 WalletExplorer 檢查實體識別

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),
    }

步驟 5:產出歸因報告

將分析結果彙整為結構化的情報報告:

勒索軟體錢包分析報告
====================================
勒索地址:        bc1q...xyz
家族歸因:        LockBit 3.0(根據贖金筆記格式)
總收款:          4.25 BTC(付款當時約 178,500 美元)
總付款:          4.25 BTC(錢包已完全清空)
付款筆數:        3 筆(可能來自 3 個不同受害者)

資金流向:
  付款 1:1.5 BTC → 合併錢包 → Binance 存款
  付款 2:1.0 BTC → Wasabi 混幣器 → 未知
  付款 3:1.75 BTC → 剝離鏈 (12 跳) → OKX 存款

集群分析:
  相關錢包:同一集群中識別出 47 個地址
  集群總交易量:156.3 BTC(約 650 萬美元)
  首次活動:2024-01-15
  最後活動:2024-09-22

驗證

  • 在查詢 API 前,先確認錢包地址格式有效
  • 將交易時間戳與已知事件時間線交叉比對
  • 透過共同輸入所有權啟發式方法驗證集群關聯
  • 將結果與 OFAC SDN 制裁地址清單比對
  • 針對多個來源(WalletExplorer、OXT、Chainalysis)驗證交易所歸因

關鍵概念

術語定義
UTXO未花費交易輸出;Bitcoin 的基本單位,透過交易鏈追蹤所有權
集群分析使用共同輸入所有權和找零地址啟發式方法,將多個 Bitcoin 地址歸為同一實體控制
剝離鏈一種洗錢模式,將資金透過多個連續交易發送,每筆交易剝離少量金額至新地址
CoinJoin/混幣器結合多位使用者的交易以混淆傳送方與接收方關聯的隱私技術
共同輸入所有權假設單一交易中的所有輸入都由同一實體控制的啟發式方法

工具與系統

  • Chainalysis Reactor:企業級區塊鏈調查平台,提供實體歸因與跨鏈追蹤功能
  • WalletExplorer:免費工具,可將 Bitcoin 地址分群並標記已知服務(交易所、混幣器、市集)
  • OXT.me:進階 Bitcoin 交易視覺化工具,具備 UTXO 圖形分析功能
  • Blockstream.info:開源 Bitcoin 區塊瀏覽器,提供完整 API 存取
  • blockchain.com API:查詢 Bitcoin 地址餘額與交易歷史的免費 API
  • OFAC SDN 清單:美國財政部制裁地址清單,用於合規檢查

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

abusing-dpapi-for-credential-access

Extract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket

abusing-shadow-credentials-for-privesc

Take over Active Directory accounts by writing attacker-controlled public keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, or Certipy, then authenticate via PKINIT to recover the target

achieving-cmmc-level-2-compliance

>-

acquiring-disk-image-with-dd-and-dcfldd

Create forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.

analyzing-active-directory-acl-abuse

Detect dangerous ACL misconfigurations in Active Directory using ldap3

analyzing-android-malware-with-apktool

Perform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and reflection-based API calls. Use to statically triage a suspicious APK without executing it or to build mobile malware detection rules.

analyzing-api-gateway-access-logs

Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect

analyzing-apt-group-with-mitre-navigator

Query ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups

analyzing-azure-activity-logs-for-threats

Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query

analyzing-bootkit-and-rootkit-samples

Analyzes bootkit and advanced rootkit malware infecting the Master

analyzing-browser-forensics-with-hindsight

Parse Chromium-based browser databases with Hindsight to extract and correlate browsing history, downloads, cookies, cached content, autofill data, saved passwords, and extensions from Chrome, Edge, Brave, Opera, and Vivaldi into a unified timeline (XLSX, JSON, or SQLite output). Use during incident response, insider-threat investigations, or criminal cases when you need to reconstruct a user

analyzing-campaign-attribution-evidence

Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.

analyzing-certificate-transparency-for-phishing

Monitor Certificate Transparency logs using crt.sh and Certstream to

analyzing-cloud-storage-access-patterns

Detect abnormal access in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics for after-hours bulk downloads, new-IP access, and API-call spikes (e.g. GetObject) via statistical baselines and time-series anomaly detection. Use when investigating suspected cloud data exfiltration or building related detection rules.

analyzing-cobalt-strike-beacon-configuration

Extract and analyze Cobalt Strike beacon configuration from PE files

analyzing-cobaltstrike-malleable-c2-profiles

Parse and analyze Cobalt Strike Malleable C2 profiles with dissect.cobaltstrike (profiles and beacon-payload configs) and pyMalleableC2 (AST parsing) to extract HTTP/DNS transforms, URIs, headers, sleep/jitter, and injection behavior, then generate network detection signatures. Use when reverse-engineering a captured malleable profile or building detections against Cobalt Strike Beacon traffic.

analyzing-command-and-control-communication

Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom

analyzing-cyber-kill-chain

Analyzes intrusion activity against the Lockheed Martin Cyber Kill Chain

analyzing-disk-image-with-autopsy

Perform comprehensive forensic analysis of raw (dd), E01, or AFF disk images with Autopsy and The Sleuth Kit, recovering deleted files, examining metadata and embedded artifacts, keyword searching, and building investigation timelines with visual reports. Use for structured analysis of a forensic disk image or when stakeholders need visual reports from evidence.

analyzing-dns-logs-for-exfiltration

Analyzes DNS query logs to detect data exfiltration via DNS tunneling,

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