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Análisis de Explotación por Heap Spray

Detecta y analiza ataques de heap spray en volcados de memoria usando Volatility3.

¿Qué es Análisis de Explotación por Heap Spray?

Esta habilidad está diseñada para detectar y analizar ataques de heap spray en volcados de memoria mediante el framework Volatility3. El heap spray es una técnica de explotación que llena grandes regiones del montón de un proceso con datos controlados por el atacante (como NOP sleds seguidos de shellcode) para aumentar la fiabilidad de las explotaciones de código. La habilidad guía al usuario a través de pasos como identificar procesos sospechosos con `windows.malfind`, analizar entradas VAD con `windows.vadinfo` en busca de asignaciones RWX contiguas, escanear regiones de memoria en busca de patrones de NOP sled (como 0x90 y 0x0c0c0c0c), y extraer el shellcode incrustado para su análisis. Es especialmente útil para analistas de seguridad que investigan incidentes, construyen reglas de detección, o realizan threat hunting. Los requisitos incluyen Python 3.9+, Volatility3 instalado, un volcado de memoria en formatos como .raw o .vmem, y conocimientos básicos sobre el diseño de memoria virtual y árboles VAD. El resultado esperado es un informe JSON con procesos sospechosos, indicadores de heap spray, ubicaciones de NOP sled, tamaños de regiones de memoria y hashes del shellcode extraído.

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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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Documentación

¿Qué hace Análisis de Explotación por Heap Spray?

Descripción General

El heap spray es una técnica de explotación que llena grandes regiones del montón de un proceso con datos controlados por el atacante (típicamente NOP sleds seguidos de shellcode) para aumentar la fiabilidad de las explotaciones de código. Esta habilidad cubre la detección de artefactos de heap spray en volcados de memoria usando los plugins malfind, vadinfo y memmap de Volatility3, la identificación de asignaciones de memoria contiguas sospechosas, el escaneo de patrones de NOP sled (0x90, 0x0c0c0c0c) y la extracción de shellcode incrustado para su análisis.

Cuándo Usarlo

  • Al investigar incidentes de seguridad que requieran analizar explotaciones por heap spray
  • Al construir reglas de detección o consultas de threat hunting para este dominio
  • Cuando los analistas de SOC necesiten procedimientos estructurados para este tipo de análisis
  • Al validar la cobertura de monitoreo de seguridad para técnicas de ataque relacionadas

Prerrequisitos

  • Python 3.9+ con el framework volatility3 instalado
  • Archivo de volcado de memoria (formato .raw, .vmem, .dmp)
  • Comprensión del diseño de memoria virtual y árboles VAD (Virtual Address Descriptor)
  • Familiaridad con patrones comunes de shellcode y codificaciones de NOP sled

Pasos

Paso 1: Identificar Procesos Sospechosos

Usa windows.malfind de Volatility3 para escanear procesos con regiones de memoria inyectadas ejecutables.

Paso 2: Analizar Entradas VAD

Examina las entradas del árbol VAD usando windows.vadinfo en busca de grandes asignaciones contiguas con permisos RWX.

Paso 3: Escanear Patrones de NOP Sled

Busca firmas de NOP sled (secuencias 0x90, patrones 0x0c0c0c0c) en regiones de memoria sospechosas.

Paso 4: Extraer y Analizar Shellcode

Volca las regiones de memoria sospechosas e identifica shellcode mediante análisis de patrones de bytes.

Resultado Esperado

Informe JSON con procesos sospechosos, indicadores de heap spray, ubicaciones de NOP sled, tamaños de regiones de memoria y hashes del 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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