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análise-de-exploração-por-heap-spray

Detecte e analise ataques de heap spray em dumps de memória usando Volatility3.

O que é análise-de-exploração-por-heap-spray?

Esta skill capacita agentes de IA e analistas de segurança a detectar e analisar ataques de heap spray em dumps de memória, usando o framework Volatility3. O heap spray é uma técnica de exploração que preenche grandes regiões do heap de um processo com dados controlados pelo atacante (como NOP sleds e shellcode) para aumentar a confiabilidade da exploração de código. A skill fornece um procedimento estruturado em quatro etapas: identificação de processos suspeitos com o plugin `malfind`, análise de entradas VAD (Virtual Address Descriptor) com `vadinfo` para encontrar alocações RWX contíguas, varredura por padrões de NOP sled (como sequências 0x90 e 0x0c0c0c0c) usando `memmap`, e extração/análise do shellcode embutido. O resultado esperado é um relatório JSON contendo processos suspeitos, indicadores de heap spray, localizações de NOP sled, tamanhos de regiões de memória e hashes do shellcode extraído. Essa skill é útil para investigadores de incidentes, SOCs e engenheiros de segurança que precisam de procedimentos reproduzíveis para análise forense de memória e caça a ameaças.

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npx skills add https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-heap-spray-exploitation
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Documentação

Analisando Exploração por Heap Spray

Visão Geral

Heap spraying é uma técnica de exploração que preenche grandes regiões do heap de um processo com dados controlados pelo atacante (tipicamente NOP sleds seguidos por shellcode) para aumentar a confiabilidade de explorações de execução de código. Esta skill cobre a detecção de artefatos de heap spray em dumps de memória usando os plugins malfind, vadinfo e memmap do Volatility3, identificando alocações de memória contíguas suspeitas, varrendo por padrões de NOP sled (0x90, 0x0c0c0c0c) e extraindo shellcode embutido para análise.

Quando Usar

  • Ao investigar incidentes de segurança que exigem análise de exploração por heap spray
  • 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

  • Python 3.9+ com o framework volatility3 instalado
  • Arquivo de dump de memória (formato .raw, .vmem, .dmp)
  • Compreensão do layout de memória virtual e árvores VAD (Virtual Address Descriptor)
  • Familiaridade com padrões comuns de shellcode e codificações de NOP sled

Passos

Passo 1: Identificar Processos Suspeitos

Use o plugin windows.malfind do Volatility3 para escanear processos com regiões de memória injetadas executáveis.

Passo 2: Analisar Entradas VAD

Examine as entradas da árvore VAD usando windows.vadinfo para encontrar alocações contíguas grandes com permissões RWX.

Passo 3: Escanear por Padrões de NOP Sled

Pesquise regiões de memória suspeitas por assinaturas de NOP sled (sequências 0x90, padrões 0x0c0c0c0c).

Passo 4: Extrair e Analisar Shellcode

Despeje regiões de memória suspeitas e identifique shellcode usando análise de padrões de bytes.

Saída Esperada

Relatório JSON com processos suspeitos, indicadores de heap spray, localizações de NOP sled, tamanhos de regiões de memória e hashes do shellcode extraído.

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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