Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add killvxk/cybersecurity-skills-zh --skill analyzing-memory-dumps-with-volatilitygit clone --depth 1 https://github.com/killvxk/cybersecurity-skills-zhWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/killvxk/cybersecurity-skills-zh/analyzing-memory-dumps-with-volatility)<a href="https://agentmods.dev/skills/killvxk/cybersecurity-skills-zh/analyzing-memory-dumps-with-volatility"><img src="https://agentmods.dev/badge/skills/killvxk/cybersecurity-skills-zh/analyzing-memory-dumps-with-volatility/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/killvxk/cybersecurity-skills-zh/analyzing-memory-dumps-with-volatility"><img src="https://agentmods.dev/badge/skills/killvxk/cybersecurity-skills-zh/analyzing-memory-dumps-with-volatility.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00097 | $0.03419 |
| Opus 5 | $0.00048 | $0.01709 |
| Sonnet 5 | $0.00019 | $0.00684 |
| Haiku 4.5 | $0.00010 | $0.00342 |
Grade A, and why
analyzing-memory-dumps-with-volatility scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 297 lines — stays where its author put it; the contents beside it link to each section on GitHub.
使用 Volatility 分析内存转储
适用场景
- 被攻陷系统的 RAM 已被捕获,需要对恶意软件工件进行取证分析
- 检测仅存在于内存中、没有持久化磁盘工件的无文件恶意软件
- 从进程内存中提取加密密钥、密码或解密后的配置
- 识别被攻陷系统中的进程注入、DLL 注入或进程空洞化
- 分析隐藏在标准磁盘取证工具之外的 Rootkit 活动
不适用于磁盘镜像分析;请使用 Autopsy、FTK 或 Sleuth Kit 进行磁盘取证。
前置条件
- Volatility 3 已安装(
pip install volatility3),并带有目标操作系统的符号表 - 从目标系统获取的内存转储文件(使用 WinPmem、LiME 或 DumpIt)
- 了解源操作系统版本,用于正确选择配置文件/符号
- 足够的磁盘空间(内存转储可达 4-64 GB)
- 用于扫描内存中已知恶意软件签名的 YARA 规则
- Strings 工具,用于从内存区域提取可读字符串
工作流程
步骤 1:识别内存转储配置文件
从内存转储中确定操作系统和版本:
# Volatility 3:自动操作系统检测
vol3 -f memory.dmp windows.info
# 列出可用插件
vol3 -f memory.dmp --help
# 如果需要符号表,从以下地址下载:
# https://downloads.volatilityfoundation.org/volatility3/symbols/
# 对于 Volatility 2(旧版):
vol2 -f memory.dmp imageinfo
vol2 -f memory.dmp kdbgscan
步骤 2:枚举运行中的进程
列出所有进程并识别可疑条目:
# 列出所有进程
vol3 -f memory.dmp windows.pslist
# 进程树(父子进程关系)
vol3 -f memory.dmp windows.pstree
# 扫描隐藏/未链接的进程(Rootkit 检测)
vol3 -f memory.dmp windows.psscan
# 比较 pslist 和 psscan 以查找隐藏进程
# 在 psscan 中但不在 pslist 中的进程可能被 Rootkit 隐藏
# 检查进程空洞化
vol3 -f memory.dmp windows.pslist --dump
# 然后验证转储的 EXE 是否与磁盘上的预期二进制文件匹配
可疑进程指标:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
- svchost.exe 不是由 services.exe 启动(父进程错误)
- csrss.exe/lsass.exe 具有异常的父进程
- lsass.exe 有多个实例(应该只有一个)
- 进程名称拼写错误(scvhost.exe、lssas.exe)
- cmd.exe 或 powershell.exe 由 WINWORD.EXE 或浏览器启动
- 从异常路径运行的进程(%TEMP%、%APPDATA%)
- 没有父进程的进程(孤立进程 - 父进程已终止)
步骤 3:检测恶意代码注入
扫描注入的代码和进程空洞化:
# 检测进程中的注入代码(malfind)
vol3 -f memory.dmp windows.malfind
# malfind 查找:
# - 具有 PAGE_EXECUTE_READWRITE 保护的内存区域
# - 包含 PE 头部(MZ/PE 签名)的内存区域
# - VAD(虚拟地址描述符)异常
# 转储注入的内存区域进行分析
vol3 -f memory.dmp windows.malfind --dump --pid 2184
# 列出每个进程加载的 DLL
vol3 -f memory.dmp windows.dlllist --pid 2184
# 通过比较映射镜像与磁盘检测空洞化进程
vol3 -f memory.dmp windows.hollowfind
# 扫描加载的驱动程序(潜在的 Rootkit 驱动程序)
vol3 -f memory.dmp windows.driverscan
# 列出内核模块
vol3 -f memory.dmp windows.modules
步骤 4:分析网络连接
提取活动和已关闭的网络连接:
# 列出所有网络连接(活动和监听)
vol3 -f memory.dmp windows.netscan
# 输出列:偏移量、协议、本地地址、本地端口、外部地址、外部端口、状态、PID、所有者
# 过滤到外部 IP 的已建立连接
vol3 -f memory.dmp windows.netscan | grep ESTABLISHED
# 对于旧版 Windows(XP/2003):
vol3 -f memory.dmp windows.netstat
# 将 PID 与进程列表交叉验证
# 可疑:svchost.exe 通过非标准端口连接到外部 IP
# 可疑:notepad.exe 或 calc.exe 有网络连接
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 297 lines · 97 tokens per session scan A 5d9bf7241bd6
analyzing-memory-dumps-with-volatility is a skill published in the GitHub repository killvxk/cybersecurity-skills-zh (44 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 97 tokens to every session and 3,419 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
analyzing-memory-dumps-with-volatility
Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis…
analyzing-memory-dumps-with-volatility
Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis…
analyzing-memory-dumps-with-volatility
Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis…
analyzing-memory-dumps-with-volatility
Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis…
analyzing-memory-dumps-with-volatility
Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis…
analyzing-memory-dumps-with-volatility
Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis…