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分析Office 365审计日志以检测入侵

通过Microsoft Graph API解析Office 365统一审计日志，检测企业邮箱入侵迹象。

分析Office 365审计日志以检测入侵 是什么？

此技能专注于利用Microsoft Graph API查询Office 365统一审计日志，以检测企业邮件入侵（BEC）的痕迹。它能够帮助安全分析师识别可疑的收件箱规则创建、邮件转发至外部地址、邮箱委派变更以及未授权的OAuth应用程序授权同意等威胁指标。该技能支持自动化检测规则构建、威胁狩猎以及安全监控覆盖验证，适用于SOC分析师和威胁研究人员。通过执行一系列结构化步骤（包括身份验证、日志查询、规则枚举和报告生成），最终输出包含风险评分的JSON报告和入侵指标时间线，显著提升对Office 365环境的安全事件响应效率。

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npx skills add https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-office365-audit-logs-for-compromise
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分析Office 365审计日志以检测入侵 是做什么的？

概述

企业邮件入侵（BEC）攻击常会在Office 365审计日志中留下痕迹：可疑的收件箱规则创建、邮件转发至外部地址、邮箱委派变更以及未授权的OAuth应用程序授权同意。本技能使用Microsoft Graph API查询统一审计日志、枚举各邮箱的收件箱规则、检测转发配置，并识别受损账户指标。

使用时机

  • 调查需要分析Office 365审计日志以检测入侵的安全事件时
  • 为此领域构建检测规则或威胁狩猎查询时
  • SOC分析师需要此类分析的结构化流程时
  • 验证针对相关攻击技术的安全监控覆盖范围时

前提条件

  • 已注册Azure AD应用，并拥有AuditLog.Read.All、MailboxSettings.Read、Mail.Read（应用程序权限）
  • Python 3.9+，并安装msal、requests库
  • 用于身份验证的客户端密钥或证书
  • 全局读取者或安全读取者角色

步骤

  1. 使用MSAL客户端凭据流程向Microsoft Graph进行身份验证
  2. 查询统一审计日志，寻找可疑操作（如Set-Mailbox、New-InboxRule）
  3. 枚举各邮箱的收件箱规则，标记转发规则
  4. 检测邮箱委派变更（如Add-MailboxPermission）
  5. 识别对可疑应用程序的OAuth授权同意
  6. 从审计日志中检查可疑的登录模式
  7. 生成包含时间线的入侵指标报告

预期输出

  • JSON格式报告，列出转发规则、委派变更、OAuth授权以及带有风险评分的可疑审计事件
  • 包含受影响邮箱的入侵指标时间线

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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