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performing-memory-forensics-with-volatility3(执行具有波动性的内存取证 3)

performing-memory-forensics-with-volatility3 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

performing-memory-forensics-with-volatility3 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 66,424。

总安装 264周安装 11GitHub Stars 5,924下载 88核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
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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-memory-forensics-with-volatility3(执行具有波动性的内存取证 3)
来源仓库:https://github.com/mukul975/anthropic-cybersecurity-skills
仓库路径:skills/performing-memory-forensics-with-volatility3
安装命令:
npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill performing-memory-forensics-with-volatility3
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-memory-forensics-with-volatility3

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

使用 volatility3 执行内存取证分析,提取运行时证据。适用于应急响应、恶意进程检测与内存转储研究。解析内存结构,查找隐藏进程、网络连接与注入代码。需具备专业技能,正确解读内存快照内容。

performing-memory-forensics-with-volatility3 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/performing-memory-forensics-with-volatility3

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skil…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

performing-memory-forensics-with-volatility3 怎么安装?

在终端执行 npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill performi…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

performing-memory-forensics-with-volatility3 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

performing-memory-forensics-with-volatility3 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

performing-memory-forensics-with-volatility3 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

performing-memory-forensics-with-volatility3 用的人多吗?

公开数据是累计安装 264 次,近一周安装 11 次,下载 88 次,GitHub 5,924 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

performing-memory-forensics-with-volatility3 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 来源仓库有 5,924 星,维护活跃度相对可查。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
performing-memory-forensics-with-volatility3

执行具有波动性的内存取证 3 · 本页

2645,924接触敏感数据未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Performing Memory Forensics with Volatility 3

When to Use

  • When analyzing a RAM dump from a compromised or suspect system
  • During incident response to identify running malware, injected code, or rootkits
  • When you need to extract credentials, encryption keys, or network connections from memory
  • For detecting process hollowing, DLL injection, or hidden processes
  • When disk-based forensics alone is insufficient and volatile data is critical

Prerequisites

  • Python 3.7+ installed
  • Volatility 3 framework installed (pip install volatility3)
  • Memory dump in raw, ELF, or crash dump format
  • Appropriate symbol tables (ISF files) for the target OS version
  • Sufficient disk space for analysis output (2-3x memory dump size)
  • Optional: YARA rules for malware scanning in memory

Workflow

Step 1: Acquire Memory Dump and Install Volatility 3

# Install Volatility 3
pip install volatility3

# Or install from source for latest features
git clone https://github.com/volatilityfoundation/volatility3.git
cd volatility3
pip install -e .

# Download Windows symbol tables (ISF packs)
# Place in volatility3/symbols/ directory
wget https://downloads.volatilityfoundation.org/volatility3/symbols/windows.zip
unzip windows.zip -d /opt/volatility3/volatility3/symbols/

# Download Linux and Mac symbol packs
wget https://downloads.volatilityfoundation.org/volatility3/symbols/linux.zip
wget https://downloads.volatilityfoundation.org/volatility3/symbols/mac.zip

# Memory acquisition tools (for live systems):
# Windows: winpmem, DumpIt, FTK Imager
# Linux: LiME (Linux Memory Extractor)
sudo insmod lime-$(uname -r).ko "path=/cases/memory/linux_mem.lime format=lime"

# Verify the memory dump
file /cases/case-2024-001/memory/memory.raw
ls -lh /cases/case-2024-001/memory/memory.raw

Step 2: Identify the Operating System Profile

# Run banners plugin to identify the OS
vol -f /cases/case-2024-001/memory/memory.raw banners

# For Windows, identify the OS version
vol -f /cases/case-2024-001/memory/memory.raw windows.info

# Output example:
# Variable        Value
# Kernel Base     0xf8047e200000
# DTB             0x1ad000
# Symbols         ntkrnlmp.pdb/GUID
# Is64Bit         True
# IsPAE           False
# primary layer   Intel32e
# KdVersionBlock  0xf8047ee232c0
# Major/Minor     15.19041
# Machine Type    34404
# KeNumberProcessors 4
# SystemTime      2024-01-18 14:32:15 UTC
# NtBuildLab      19041.1.amd64fre.vb_release.191206-1406
# NtProductType   NtProductWinNt
# NtSystemRoot    C:\WINDOWS
# PE MajorOperatingSystemVersion 10
# PE MinorOperatingSystemVersion 0

# For Linux memory dumps
vol -f /cases/case-2024-001/memory/linux_mem.lime linux.info

Step 3: Enumerate Processes and Detect Anomalies

# List all running processes
vol -f /cases/case-2024-001/memory/memory.raw windows.pslist | tee /cases/case-2024-001/analysis/pslist.txt

# Show process tree (parent-child relationships)
vol -f /cases/case-2024-001/memory/memory.raw windows.pstree | tee /cases/case-2024-001/analysis/pstree.txt

# Detect hidden processes using cross-view analysis
vol -f /cases/case-2024-001/memory/memory.raw windows.psscan | tee /cases/case-2024-001/analysis/psscan.txt

# Compare pslist vs psscan to find hidden processes
diff <(vol -f memory.raw windows.pslist | awk '{print $1}' | sort) \
     <(vol -f memory.raw windows.psscan | awk '{print $1}' | sort)

# List DLLs loaded by a suspicious process (PID 4532)
vol -f /cases/case-2024-001/memory/memory.raw windows.dlllist --pid 4532

# Check for process hollowing and injection
vol -f /cases/case-2024-001/memory/memory.raw windows.malfind | tee /cases/case-2024-001/analysis/malfind.txt

# Dump suspicious process memory for further analysis
vol -f /cases/case-2024-001/memory/memory.raw windows.memmap --pid 4532 --dump \
   -o /cases/case-2024-001/analysis/dumps/

Step 4: Analyze Network Connections and Registry

# List active network connections
vol -f /cases/case-2024-001/memory/memory.raw windows.netscan | tee /cases/case-2024-001/analysis/netscan.txt

# Filter for established connections
vol -f /cases/case-2024-001/memory/memory.raw windows.netscan | grep ESTABLISHED

# Filter for listening ports
vol -f /cases/case-2024-001/memory/memory.raw windows.netscan | grep LISTENING

# Extract network connections with process mapping
vol -f /cases/case-2024-001/memory/memory.raw windows.netstat | tee /cases/case-2024-001/analysis/netstat.txt

# Dump registry hives from memory
vol -f /cases/case-2024-001/memory/memory.raw windows.registry.hivelist

# Extract specific registry keys
vol -f /cases/case-2024-001/memory/memory.raw windows.registry.printkey \
   --key "Software\Microsoft\Windows\CurrentVersion\Run"

# Check services
vol -f /cases/case-2024-001/memory/memory.raw windows.svcscan | tee /cases/case-2024-001/analysis/services.txt

Step 5: Extract Credentials and Sensitive Data

# Dump cached credentials (hashdump)
vol -f /cases/case-2024-001/memory/memory.raw windows.hashdump | tee /cases/case-2024-001/analysis/hashes.txt

# Extract LSA secrets
vol -f /cases/case-2024-001/memory/memory.raw windows.lsadump

# Dump cached domain credentials
vol -f /cases/case-2024-001/memory/memory.raw windows.cachedump

# Search for plaintext strings in process memory
vol -f /cases/case-2024-001/memory/memory.raw windows.strings --pid 4532 \
   | grep -iE '(password|credential|token|api.key)'

# Extract command history from cmd.exe/powershell
vol -f /cases/case-2024-001/memory/memory.raw windows.cmdline | tee /cases/case-2024-001/analysis/cmdline.txt

# Extract environment variables
vol -f /cases/case-2024-001/memory/memory.raw windows.envars --pid 4532

Step 6: Scan for Malware with YARA Rules

# Scan memory with YARA rules
vol -f /cases/case-2024-001/memory/memory.raw yarascan \
   --yara-file /opt/yara-rules/malware_index.yar | tee /cases/case-2024-001/analysis/yara_hits.txt

# Scan specific process memory
vol -f /cases/case-2024-001/memory/memory.raw yarascan \
   --yara-file /opt/yara-rules/apt_rules.yar --pid 4532

# Check loaded kernel modules for rootkits
vol -f /cases/case-2024-001/memory/memory.raw windows.modules | tee /cases/case-2024-001/analysis/modules.txt

# Detect unlinked/hidden modules
vol -f /cases/case-2024-001/memory/memory.raw windows.modscan | tee /cases/case-2024-001/analysis/modscan.txt

# Check for SSDT hooks (System Service Descriptor Table)
vol -f /cases/case-2024-001/memory/memory.raw windows.ssdt | grep -v "ntoskrnl\|win32k"

# Dump a suspicious executable from memory
vol -f /cases/case-2024-001/memory/memory.raw windows.dumpfiles --pid 4532 \
   -o /cases/case-2024-001/analysis/extracted/

Step 7: Compile Findings into a Report

# Generate comprehensive analysis summary
echo "=== MEMORY FORENSICS REPORT ===" > /cases/case-2024-001/analysis/memory_report.txt
echo "Image: memory.raw" >> /cases/case-2024-001/analysis/memory_report.txt
echo "OS: Windows 10 Build 19041" >> /cases/case-2024-001/analysis/memory_report.txt
echo "" >> /cases/case-2024-001/analysis/memory_report.txt

echo "--- Suspicious Processes ---" >> /cases/case-2024-001/analysis/memory_report.txt
cat /cases/case-2024-001/analysis/malfind.txt >> /cases/case-2024-001/analysis/memory_report.txt

echo "--- Network Connections ---" >> /cases/case-2024-001/analysis/memory_report.txt
cat /cases/case-2024-001/analysis/netscan.txt >> /cases/case-2024-001/analysis/memory_report.txt

echo "--- YARA Matches ---" >> /cases/case-2024-001/analysis/memory_report.txt
cat /cases/case-2024-001/analysis/yara_hits.txt >> /cases/case-2024-001/analysis/memory_report.txt

# Calculate hash of the memory dump for integrity
sha256sum /cases/case-2024-001/memory/memory.raw >> /cases/case-2024-001/analysis/memory_report.txt

Key Concepts

ConceptDescription
Volatile dataInformation that exists only in RAM and is lost when power is removed
Process hollowingTechnique where malware replaces legitimate process memory with malicious code
DLL injectionLoading unauthorized DLLs into a running process address space
EPROCESSWindows kernel structure representing a process; basis for process listing
Pool scanningSearching memory for kernel object signatures to find hidden artifacts
VAD (Virtual Address Descriptor)Memory management structure tracking process virtual memory regions
ISF (Intermediate Symbol Format)Volatility 3 symbol table format for OS-specific structure definitions
MalfindPlugin detecting injected code by examining VAD permissions and content

Tools & Systems

ToolPurpose
Volatility 3Primary open-source memory forensics framework
LiMELinux Memory Extractor for acquiring Linux RAM dumps
WinPmemWindows physical memory acquisition driver
DumpItComae one-click Windows memory dump utility
YARAPattern matching engine for malware signature scanning
RekallAlternative memory forensics framework (Google)
MemProcFSMemory process file system for memory analysis
stringsExtract printable strings from binary memory dumps

Common Scenarios

Scenario 1: Active Malware Investigation Acquire memory with DumpIt, run pslist/pstree to identify suspicious processes, use malfind to detect injected code in svchost.exe, dump the injected memory segment, scan with YARA rules identifying Cobalt Strike beacon, extract C2 IP from netscan, correlate with network logs.

Scenario 2: Credential Theft After Breach Run hashdump and lsadump to extract cached credentials, identify mimikatz execution in cmdline output, check for lsass.exe memory dumps in filesystem artifacts, correlate with lateral movement evidence in network connections.

Scenario 3: Rootkit Detection Compare pslist (uses EPROCESS linked list) with psscan (pool scanning) to find unlinked processes, check modules vs modscan for hidden kernel drivers, examine SSDT for hooks redirecting system calls, dump suspicious modules for static analysis.

Scenario 4: Ransomware Incident Recovery Extract encryption keys from ransomware process memory before system shutdown, identify the ransomware variant using YARA, find the initial execution point through command line artifacts, map lateral movement via network connections.

Output Format

Memory Forensics Analysis:
  Image:            memory.raw (16 GB)
  OS Identified:    Windows 10 x64 Build 19041
  Capture Time:     2024-01-18 14:32:15 UTC

  Process Analysis:
    Total Processes:    87
    Hidden Processes:   2 (PIDs: 4532, 6128)
    Injected Processes: 3 (malfind detections)
    Suspicious:         svchost.exe (PID 4532) - injected code at 0x7FFE0000

  Network Connections:
    Total:        45
    Established:  12
    Suspicious:   3 (C2 connections to 185.xx.xx.xx:443)

  Credentials Found:
    NTLM Hashes:      4 accounts
    Cached Creds:      2 domain accounts

  YARA Matches:
    CobaltStrike_Beacon:  PID 4532 (3 hits)
    Mimikatz_Memory:      PID 6128 (1 hit)

  Extracted Artifacts:   15 files dumped to /analysis/extracted/

平台分布

Codex

36.98%
按下载量换算33

Claude

28.4%
按下载量换算25

Cursor

20.03%
按下载量换算18

Gemini CLI

10.16%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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