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performing-endpoint-forensics-investigation进行端点取证调查

Agent Skill

用于辅助 API 设计、接口文档、请求响应结构和服务集成说明。它适合让 Agent 梳理 endpoint、生成 OpenAPI 草稿、检查字段命名、整理错误码或辅助前后端联调。使用时需要确认真实业务语义、鉴权方式、分页和错误处理规则;涉及生成接口文档时,应避免凭空补字段,最好从现有代码、schema 或接口样例中提取事实。

总安装

256

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:performing-endpoint-forensics-investigation(进行端点取证调查)
来源仓库:https://github.com/mukul975/anthropic-cybersecurity-skills
仓库路径:skills/performing-endpoint-forensics-investigation
安装命令:
npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill performing-endpoint-forensics-investigation
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill performing-endpoint-forensics-investigation

简介

进行端点取证调查,支持日志分析与事件溯源。

  • 适用于安全事件响应、入侵检测与系统审计。
  • 提取并解析端点日志,识别异常活动与攻击痕迹。
  • 操作前应获取合法授权,注意保护用户隐私与数据安全。
  • performing-endpoint-forensics-investigation 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Performing Endpoint Forensics Investigation

When to Use

Use this skill when:

  • Investigating a confirmed or suspected endpoint compromise requiring forensic analysis
  • Collecting volatile and non-volatile evidence for incident response or legal proceedings
  • Analyzing memory dumps for malware, injected code, or credential theft artifacts
  • Reconstructing attacker timelines from endpoint artifacts (prefetch, shimcache, amcache)

Do not use this skill for live threat hunting (use EDR/SIEM) or network forensics.

Prerequisites

  • Forensic workstation with analysis tools (Volatility 3, KAPE, Autopsy, Eric Zimmerman tools)
  • Write-blocker for disk imaging (hardware or software)
  • Secure evidence storage with chain-of-custody documentation
  • Memory acquisition tool (WinPMEM, FTK Imager, Magnet RAM Capture)
  • Administrative access to the target endpoint (or physical access)

Workflow

Step 1: Evidence Preservation (Order of Volatility)

Collect evidence from most volatile to least volatile:

1. System memory (RAM) - Most volatile
2. Network connections and routing tables
3. Running processes and open files
4. Disk contents (file system)
5. Removable media
6. Logs and backup data - Least volatile

Memory Acquisition:

# WinPMEM (Windows)
winpmem_mini_x64.exe memdump.raw

# FTK Imager - Create memory capture via GUI
# File → Capture Memory → Destination path → Capture Memory

# Linux (LiME kernel module)
sudo insmod lime.ko "path=/evidence/memory.lime format=lime"

Volatile Data Collection:

# Capture running processes
Get-Process | Export-Csv "evidence\processes.csv" -NoTypeInformation
tasklist /v > "evidence\tasklist.txt"

# Capture network connections
netstat -anob > "evidence\netstat.txt"
Get-NetTCPConnection | Export-Csv "evidence\tcp_connections.csv"

# Capture logged-on users
query user > "evidence\logged_users.txt"

# Capture scheduled tasks
schtasks /query /fo CSV /v > "evidence\scheduled_tasks.csv"

# Capture services
Get-Service | Export-Csv "evidence\services.csv"

# Capture DNS cache
ipconfig /displaydns > "evidence\dns_cache.txt"

Step 2: Disk Imaging

# FTK Imager - Create forensic disk image
# File → Create Disk Image → Physical Drive → E01 format
# Always verify image hash (MD5/SHA1) matches source

# dd (Linux)
sudo dc3dd if=/dev/sda of=/evidence/disk.dd hash=sha256 log=/evidence/imaging.log

# Verify image integrity
sha256sum /evidence/disk.dd
# Compare with hash generated during imaging

Step 3: Memory Analysis with Volatility 3

# Identify OS profile
vol -f memdump.raw windows.info

# List running processes
vol -f memdump.raw windows.pslist
vol -f memdump.raw windows.pstree

# Find hidden processes
vol -f memdump.raw windows.psscan

# Analyze network connections
vol -f memdump.raw windows.netscan

# Detect process injection
vol -f memdump.raw windows.malfind

# Extract command line arguments
vol -f memdump.raw windows.cmdline

# Analyze DLLs loaded by processes
vol -f memdump.raw windows.dlllist --pid 1234

# Extract files from memory
vol -f memdump.raw windows.filescan | grep -i "suspicious"
vol -f memdump.raw windows.dumpfiles --pid 1234

# Detect credential theft
vol -f memdump.raw windows.hashdump
vol -f memdump.raw windows.lsadump

# Registry analysis from memory
vol -f memdump.raw windows.registry.printkey --key "Software\Microsoft\Windows\CurrentVersion\Run"

Step 4: Windows Artifact Analysis

Key forensic artifacts and their tools:

Prefetch Files (C:\Windows\Prefetch\):
  Tool: PECmd.exe (Eric Zimmerman)
  Shows: Program execution history with timestamps and run counts
  Command: PECmd.exe -d "C:\Windows\Prefetch" --csv output\

ShimCache (AppCompatCache):
  Tool: AppCompatCacheParser.exe
  Shows: Programs that existed on system (even if deleted)
  Command: AppCompatCacheParser.exe -f SYSTEM --csv output\

AmCache (C:\Windows\appcompat\Programs\Amcache.hve):
  Tool: AmcacheParser.exe
  Shows: Program execution with SHA1 hashes and install timestamps
  Command: AmcacheParser.exe -f Amcache.hve --csv output\

NTFS artifacts ($MFT, $UsnJrnl, $LogFile):
  Tool: MFTECmd.exe
  Shows: Complete file system timeline including deleted files
  Command: MFTECmd.exe -f "$MFT" --csv output\

Event Logs:
  Tool: EvtxECmd.exe
  Shows: Security, System, PowerShell, Sysmon events
  Command: EvtxECmd.exe -d "C:\Windows\System32\winevt\Logs" --csv output\

Registry Hives (SAM, SYSTEM, SOFTWARE, NTUSER.DAT):
  Tool: RECmd.exe with batch files
  Shows: User accounts, services, installed software, USB history
  Command: RECmd.exe -d "C:\Windows\System32\config" --bn BatchExamples\RECmd_Batch_MC.reb --csv output\

Step 5: Timeline Reconstruction

# Use KAPE for automated artifact collection
kape.exe --tsource C: --tdest C:\evidence\kape_output \
  --target KapeTriage --module !EZParser

# Create super timeline with plaso/log2timeline
log2timeline.py timeline.plaso disk_image.E01
psort.py -o l2tcsv timeline.plaso -w timeline.csv

# Filter timeline around incident timeframe
psort.py -o l2tcsv timeline.plaso "date > '2026-02-20' AND date < '2026-02-22'" -w filtered_timeline.csv

Step 6: Document Findings

Structure forensic report:

1. Executive Summary
2. Scope and Methodology
3. Evidence Inventory (with chain of custody)
4. Timeline of Events
5. Findings and Analysis
   - Initial access vector
   - Persistence mechanisms
   - Lateral movement
   - Data access/exfiltration
6. Indicators of Compromise (IOCs)
7. Recommendations
8. Appendices (tool output, hashes, raw evidence)

Key Concepts

TermDefinition
Order of VolatilityEvidence collection priority from most volatile (RAM) to least volatile (backups)
Chain of CustodyDocumented record of evidence handling from collection to presentation
Write BlockerHardware or software device that prevents modification of source evidence
Super TimelineConsolidated chronological view of all artifact timestamps for incident reconstruction
PrefetchWindows artifact recording program execution history
ShimCacheApplication compatibility artifact tracking program existence on endpoint

Tools & Systems

  • Volatility 3: Memory forensics framework for analyzing RAM dumps
  • KAPE (Kroll Artifact Parser and Extractor): Automated triage collection and parsing
  • Eric Zimmerman Tools: Suite of Windows artifact parsers (PECmd, MFTECmd, RECmd, etc.)
  • Autopsy/Sleuth Kit: Disk forensics platform for file system analysis
  • FTK Imager: Forensic imaging and memory acquisition tool
  • Plaso/log2timeline: Super timeline creation framework

Common Pitfalls

  • Modifying evidence on live system: Always image before analysis. Running tools on a live system alters timestamps and memory state.
  • Forgetting chain of custody: Evidence without documented chain of custody is inadmissible in legal proceedings.
  • Analyzing only disk, ignoring memory: In-memory-only malware (fileless attacks) leaves no disk artifacts. Always capture memory first.
  • Not hashing evidence: All evidence must be cryptographically hashed at collection time to prove integrity.
  • Tunnel vision: Focusing on one artifact when the timeline tells a broader story. Always build a comprehensive timeline.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

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能力 3

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能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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安装前确认

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来源信息

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