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Análise de Memória Forense com LiME e Volatility

Realiza aquisição de memória Linux usando o LiME (Linux Memory Extractor) e análise com Volatility 3.

O que é Análise de Memória Forense com LiME e Volatility?

Esta skill capacita agentes de IA a realizar análises forenses de memória Linux, utilizando o módulo kernel LiME para aquisição de memória e o Volatility 3 para extração e análise de artefatos forenses. É especialmente útil em investigações de incidentes de segurança, onde a análise da memória volátil permite detectar processos maliciosos, conexões de rede suspeitas, comandos bash executados e módulos kernel ocultos (rootkits). A skill suporta plataformas como Claude Code, Cursor e Codex, e fornece comandos e scripts Python prontos para uso. O fluxo de trabalho inclui desde a inserção do módulo LiME com o kernel atual até a execução de plugins do Volatility 3 como linux.pslist, linux.bash, linux.sockstat e linux.lsmod. O agente pode seguir os passos estruturados para adquirir, analisar e documentar os artefatos de memória, automatizando tarefas repetitivas para analistas SOC.

Funciona com~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-memory-forensics-with-lime-and-volatility
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Documentação

O que Análise de Memória Forense com LiME e Volatility faz?

Quando Usar

  • Ao investigar incidentes de segurança que exijam análise de memória forense com LiME e Volatility
  • Ao construir regras de detecção ou consultas de caça a ameaças para este domínio
  • Quando analistas de SOC precisam de procedimentos estruturados para este tipo de análise
  • Ao validar a cobertura de monitoramento de segurança para técnicas de ataque relacionadas

Pré-requisitos

  • Familiaridade com conceitos e ferramentas de operações de segurança
  • Acesso a um ambiente de teste ou laboratório para execução segura
  • Python 3.8+ com as dependências necessárias instaladas
  • Autorização apropriada para quaisquer atividades de teste

Instruções

Adquira memória Linux usando o módulo kernel LiME, em seguida analise com Volatility 3 para extrair artefatos forenses da imagem de memória.

# Aquisição com LiME
insmod lime-$(uname -r).ko "path=/evidencias/memoria.lime format=lime"

# Análise com Volatility 3
vol3 -f /evidencias/memoria.lime linux.pslist
vol3 -f /evidencias/memoria.lime linux.bash
vol3 -f /evidencias/memoria.lime linux.sockstat

import volatility3
from volatility3.framework import contexts, automagic
from volatility3.plugins.linux import pslist, bash, sockstat

# Uso programático do Volatility 3
context = contexts.Context()
automagics = automagic.available(context)

Principais etapas de análise:

  1. Adquirir memória com LiME (formato=lime ou formato=raw)
  2. Listar processos com linux.pslist, comparar com linux.psscan
  3. Extrair histórico de comandos bash com linux.bash
  4. Listar conexões de rede com linux.sockstat
  5. Verificar módulos kernel carregados com linux.lsmod para rootkits

Exemplos

# Fluxo de trabalho forense completo
vol3 -f memoria.lime linux.pslist | grep -v "\[kthread\]"
vol3 -f memoria.lime linux.bash
vol3 -f memoria.lime linux.malfind
vol3 -f memoria.lime linux.lsmod

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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