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分析以太坊智能合約漏洞

使用靜態和符號執行分析 Solidity 智能合約。

分析以太坊智能合約漏洞 是什麼?

此技能專注於分析以太坊智能合約中的安全性漏洞,結合 Slither 和 Mythril 兩種工具進行檢測。Slither 使用中間表示法進行快速靜態分析,可在數秒內偵測超過 90 種漏洞模式;而 Mythril 則利用符號執行與 SMT 求解器,探索重入攻擊、整數溢位等複雜的執行路徑漏洞。支援的平臺包括 Slither 與 Mythril,且可與 Foundry、Hardhat 等開發框架搭配使用。技能涵蓋從執行分析、關聯發現、嚴重性排序到生成結構化稽核報告的完整流程,適合安全事件調查、威脅狩獵或 SOC 分析使用。最終產出為 JSON 格式報告,包含 SWC 識別碼、嚴重性評級、受影響函式與修復建議,幫助開發者在合約部署前有效降低風險。

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說明文件

分析以太坊智能合約漏洞 是做什麼的?

概述

智能合約漏洞已在 DeFi 協議中造成數十億美元的損失。與傳統軟體不同,已部署的智能合約不可變更且處理真實金融資產,因此部署前的安全分析至關重要。Slither 使用中間表示法進行快速靜態分析,可在數秒內偵測超過 90 種漏洞模式;而 Mythril 則使用符號執行與 SMT 求解器,探索如重入攻擊和整數溢位等複雜的執行路徑漏洞。此技能涵蓋對 Solidity 合約執行這兩種工具、解讀結果、依嚴重性分類發現,以及生成稽核報告。

使用時機

  • 調查需要分析以太坊智能合約漏洞的安全事件時
  • 為此領域建立偵測規則或威脅狩獵查詢時
  • SOC 分析師需要此分析類型的結構化程序時
  • 驗證相關攻擊技術的安全監控覆蓋範圍時

先決條件

  • Python 3.10+ 與 pip
  • Slither(pip install slither-analyzer)和 solc 編譯器
  • Mythril(pip install mythril)與用於編譯器版本管理的 solc-select
  • Solidity 原始碼或編譯後的合約位元組碼
  • Foundry 或 Hardhat 開發框架(可選,用於專案層級分析)

步驟

步驟 1:執行 Slither 靜態分析

對合約程式碼庫執行 Slither,利用其 90+ 內建偵測器識別漏洞模式、最佳化機會與程式碼品質問題。

步驟 2:執行 Mythril 符號執行

執行 Mythril 深度分析,探索執行路徑以發現需要路徑敏感分析的重入攻擊、未檢查的外部呼叫及算術漏洞。

步驟 3:分類與關聯發現

結合兩個工具的結果,去重發現,根據可利用性與財務影響評估嚴重性,並過濾誤報。

步驟 4:生成稽核報告

產出一份結構化的稽核報告,包含漏洞描述、受影響的程式碼位置、利用場景以及修復建議。

預期輸出

JSON 格式報告,列出帶有 SWC(智能合約弱點分類)識別碼、嚴重性評級、受影響函式及建議修復的漏洞。

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

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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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