Communitygithub.com

building-ioc-defanging-and-sharing-pipeline

Construye un pipeline automatizado que ingiere IOCs sin procesar (URLs, IPs, dominios, etc.), los desarma de forma segura y los comparte en formatos STIX 2.1.

¿Qué es building-ioc-defanging-and-sharing-pipeline?

Esta habilidad te permite construir un pipeline automatizado para ingerir, normalizar, desarmar (defang) y compartir Indicadores de Compromiso (IOCs) de forma segura. Procesa IOCs desde texto libre o fuentes externas, los refang y normaliza, aplica técnicas de defanging para evitar clics accidentales y los convierte al formato STIX 2.1 para consumo por máquinas. Soporta la distribución a través de servidores TAXII o instancias MISP, generando informes legibles por humanos. Es ideal para equipos de ciberseguridad, analistas de amenazas y arquitectos de seguridad que necesitan integrar inteligencia de amenazas de manera eficiente y segura en su infraestructura.

Compatible con~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/building-ioc-defanging-and-sharing-pipeline

Preguntar en tu IA favorita

Abre un nuevo chat con esta habilidad de agente ya precargada.

Documentación

Construcción de un Pipeline de Desarme (Defanging) y Compartición de IOCs

Resumen

El desarme (defanging) de IOCs modifica los indicadores potencialmente maliciosos (URLs, direcciones IP, dominios, direcciones de correo electrónico) para evitar clics o ejecuciones accidentales, manteniendo su legibilidad para el análisis y la compartición. Esta habilidad cubre la construcción de un pipeline automatizado que ingiere IOCs sin procesar desde múltiples fuentes, los normaliza y deduplica, aplica el desarme para un consumo humano seguro, los convierte al formato STIX 2.1 para consumo por máquinas y los distribuye a través de servidores TAXII, instancias MISP e informes por correo electrónico.

Cuándo Usarlo

  • Al implementar o configurar capacidades de pipeline de desarme y compartición de IOCs en tu entorno
  • Al establecer controles de seguridad alineados con requisitos de cumplimiento
  • Al construir o mejorar la arquitectura de seguridad para este dominio
  • Al realizar evaluaciones de seguridad que requieran esta implementación

Prerrequisitos

  • Python 3.9+ con las librerías defang, ioc-fanger, stix2, requests, validators
  • Instancia MISP o servidor TAXII para compartición automatizada
  • Comprensión de tipos de IOC: IPv4/IPv6, dominios, URLs, direcciones de correo electrónico, hashes de archivos
  • Familiaridad con los patrones de indicadores STIX 2.1 y las marcas de definición TLP
  • Acceso a fuentes de inteligencia de amenazas para la ingesta de IOCs

Conceptos Clave

Estándares de Desarme (Defanging)

El desarme reemplaza componentes activos de protocolo y dominio para evitar la ejecución: http:// se convierte en hxxp://, https:// se convierte en hxxps://, los puntos en dominios/IPs se convierten en [.], y @ en correos se convierte en [@]. Esto es crítico para compartir IOCs en informes, correos electrónicos, canales de Slack y sitios de paste donde el autoenlace podría desencadenar conexiones de red a infraestructura maliciosa.

Normalización de IOCs

Los IOCs sin procesar de diferentes fuentes vienen en formatos inconsistentes. La normalización implica convertir a minúsculas, eliminar barras inclinadas y espacios en blanco finales, extraer dominios de URLs, resolver la codificación de URLs, validar la corrección del formato y deduplicar entre fuentes.

Patrones de Indicadores STIX 2.1

Los patrones STIX expresan los IOCs en un formato estandarizado: [ipv4-addr:value = '203.0.113.1'], [domain-name:value = 'malicious.example.com'], [url:value = 'http://evil.com/payload'], [file:hashes.'SHA-256' = 'abc123...']. Cada indicador incluye valid_from, indicator_types, confidence y marcas TLP opcionales.

Flujo de Trabajo

Paso 1: Construir la Extracción y Normalización de IOCs

import re
import hashlib
from urllib.parse import urlparse, unquote
from datetime import datetime

class IOCExtractor:
    """Extraer y normalizar IOCs de texto."""

    PATTERNS = {
        "ipv4": r'\b(?:(?:25[0-5]|2[0-4]\d|1\d{2}|[1-9]?\d)\.){3}(?:25[0-5]|2[0-4]\d|1\d{2}|[1-9]?\d)\b',
        "domain": r'\b(?:[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?\.)+[a-zA-Z]{2,}\b',
        "url": r'https?://[^\s<>"{}|\\^`\[\]]+',
        "email": r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
        "md5": r'\b[a-fA-F0-9]{32}\b',
        "sha1": r'\b[a-fA-F0-9]{40}\b',
        "sha256": r'\b[a-fA-F0-9]{64}\b',
    }

    WHITELIST_DOMAINS = {
        "google.com", "microsoft.com", "amazon.com", "github.com",
        "cloudflare.com", "akamai.com", "example.com",
    }

    def extract_from_text(self, text):
        """Extraer todos los tipos de IOC de texto libre."""
        # Refangear indicadores ya desarmados primero
        text = self._refang(text)
        iocs = {"ipv4": set(), "domain": set(), "url": set(),
                "email": set(), "md5": set(), "sha1": set(), "sha256": set()}

        for ioc_type, pattern in self.PATTERNS.items():
            matches = re.findall(pattern, text)
            for match in matches:
                normalized = self._normalize(match, ioc_type)
                if normalized and not self._is_whitelisted(normalized, ioc_type):
                    iocs[ioc_type].add(normalized)

        # Eliminar dominios que son parte de URLs
        url_domains = set()
        for url in iocs["url"]:
            parsed = urlparse(url)
            url_domains.add(parsed.netloc)
        iocs["domain"] -= url_domains

        total = sum(len(v) for v in iocs.values())
        print(f"[+] Extraídos {total} IOCs únicos del texto")
        return {k: sorted(v) for k, v in iocs.items()}

    def _refang(self, text):
        """Convertir indicadores desarmados de nuevo a forma activa."""
        text = text.replace("hxxp://", "http://").replace("hxxps://", "https://")
        text = text.replace("[.]", ".").replace("[@]", "@")
        text = text.replace("[://]", "://").replace("(.)", ".")
        return text

    def _normalize(self, value, ioc_type):
        """Normalizar un valor de IOC."""
        value = value.strip().lower()
        if ioc_type == "url":
            value = unquote(value).rstrip("/")
        elif ioc_type == "domain":
            value = value.rstrip(".")
        return value

    def _is_whitelisted(self, value, ioc_type):
        """Comprobar si el IOC está en la lista blanca."""
        if ioc_type == "domain":
            return value in self.WHITELIST_DOMAINS
        if ioc_type == "url":
            parsed = urlparse(value)
            return parsed.netloc in self.WHITELIST_DOMAINS
        return False

extractor = IOCExtractor()
sample_text = """
Malware C2: hxxps://evil-domain[.]com/beacon
Drops payload from 192.168.1.100 and contacts 10[.]0[.]0[.]1
SHA256: 275a021bbfb6489e54d471899f7db9d1663fc695ec2fe2a2c4538aabf651fd0f
Phishing email from attacker[@]phishing-domain[.]com
"""
iocs = extractor.extract_from_text(sample_text)

Paso 2: Motor de Desarme (Defanging)

class IOCDefanger:
    """Desarmar IOCs para compartir de forma segura en informes y comunicaciones."""

    def defang_url(self, url):
        return url.replace("http://", "hxxp://").replace("https://", "hxxps://").replace(".", "[.]")

    def defang_domain(self, domain):
        return domain.replace(".", "[.]")

    def defang_ip(self, ip):
        return ip.replace(".", "[.]")

    def defang_email(self, email):
        return email.replace("@", "[@]").replace(".", "[.]")

    def defang_all(self, iocs):
        """Desarmar todos los IOCs en un diccionario."""
        defanged = {}
        for ioc_type, values in iocs.items():
            if ioc_type == "url":
                defanged[ioc_type] = [self.defang_url(v) for v in values]
            elif ioc_type == "domain":
                defanged[ioc_type] = [self.defang_domain(v) for v in values]
            elif ioc_type == "ipv4":
                defanged[ioc_type] = [self.defang_ip(v) for v in values]
            elif ioc_type == "email":
                defanged[ioc_type] = [self.defang_email(v) for v in values]
            else:
                defanged[ioc_type] = values  # Los hashes no necesitan desarme
        return defanged

    def generate_sharing_report(self, iocs, defanged, report_name="IOC Report"):
        """Generar un informe de IOCs desarmados legible por humanos."""
        report = f"# {report_name}\n"
        report += f"Generated: {datetime.now().isoformat()}\n\n"

        for ioc_type in ["url", "domain", "ipv4", "email", "sha256", "sha1", "md5"]:
            values = defanged.get(ioc_type, [])
            if values:
                report += f"## {ioc_type.upper()} ({len(values)})\n"
                for v in values:
                    report += f"- `{v}`\n"
                report += "\n"

        return report

defanger = IOCDefanger()
defanged = defanger.defang_all(iocs)
report = defanger.generate_sharing_report(iocs, defanged, "Malware Campaign IOCs")
print(report)

Paso 3: Conversión a Formato STIX 2.1

from stix2 import Indicator, Bundle, TLP_WHITE, TLP_GREEN, TLP_AMBER
from datetime import datetime

class STIXConverter:
    """Convertir IOCs sin procesar a objetos Indicator de STIX 2.1."""

    TLP_MAP = {"white": TLP_WHITE, "green": TLP_GREEN, "amber": TLP_AMBER}

    def iocs_to_stix(self, iocs, tlp="green", confidence=75):
        """Convertir diccionario de IOCs a un bundle STIX 2.1."""
        stix_objects = []
        marking = self.TLP_MAP.get(tlp, TLP_GREEN)

        for ip in iocs.get("ipv4", []):
            stix_objects.append(Indicator(
                name=f"Malicious IP: {ip}",
                pattern=f"[ipv4-addr:value = '{ip}']",
                pattern_type="stix",
                valid_from=datetime.now(),
                indicator_types=["malicious-activity"],
                confidence=confidence,
                object_marking_refs=[marking],
            ))

        for domain in iocs.get("domain", []):
            stix_objects.append(Indicator(
                name=f"Malicious Domain: {domain}",
                pattern=f"[domain-name:value = '{domain}']",
                pattern_type="stix",
                valid_from=datetime.now(),
                indicator_types=["malicious-activity"],
                confidence=confidence,
                object_marking_refs=[marking],
            ))

        for url in iocs.get("url", []):
            escaped = url.replace("'", "\\'")
            stix_objects.append(Indicator(
                name=f"Malicious URL: {url[:60]}",
                pattern=f"[url:value = '{escaped}']",
                pattern_type="stix",
                valid_from=datetime.now(),
                indicator_types=["malicious-activity"],
                confidence=confidence,
                object_marking_refs=[marking],
            ))

        for sha256 in iocs.get("sha256", []):
            stix_objects.append(Indicator(
                name=f"Malicious File Hash: {sha256[:16]}...",
                pattern=f"[file:hashes.'SHA-256' = '{sha256}']",
                pattern_type="stix",
                valid_from=datetime.now(),
                indicator_types=["malicious-activity"],
                confidence=confidence,
                object_marking_refs=[marking],
            ))

        bundle = Bundle(objects=stix_objects)
        print(f"[+] Creado bundle STIX con {len(stix_objects)} indicadores")
        return bundle

converter = STIXConverter()
stix_bundle = converter.iocs_to_stix(iocs, tlp="amber", confidence=80)
with open("iocs_stix_bundle.json", "w") as f:
    f.write(stix_bundle.serialize(pretty=True))

Paso 4: Distribución a través de MISP y TAXII

import requests
import json
import os

class IOCDistributor:
    """Distribuir IOCs a través de varios canales."""

    def push_to_misp(self, iocs, misp_url, misp_key, event_info):
        """Enviar IOCs a MISP como un nuevo evento."""
        headers = {
            "Authorization": misp_key,
            "Content-Type": "application/json",
            "Accept": "application/json",
        }
        event = {
            "Event": {
                "info": event_info,
                "distribution": "1",  # Solo esta comunidad
                "threat_level_id": "2",  # Medio
                "analysis": "2",  # Completado
                "Attribute": [],
            }
        }

        type_mapping = {
            "ipv4": "ip-dst",
            "domain": "domain",
            "url": "url",
            "email": "email-src",
            "md5": "md5",
            "sha1": "sha1",
            "sha256": "sha256",
        }

        for ioc_type, values in iocs.items():
            misp_type = type_mapping.get(ioc_type)
            if misp_type:
                for value in values:
                    event["Event"]["Attribute"].append({
                        "type": misp_type,
                        "value": value,
                        "category": "Network activity" if ioc_type in ("ipv4", "domain", "url") else "Payload delivery",
                        "to_ids": True,
                    })

        resp = requests.post(
            f"{misp_url}/events",
            headers=headers,
            json=event,
            verify=not os.environ.get("SKIP_TLS_VERIFY", "").lower() == "true",  # Establecer SKIP_TLS_VERIFY=true para certificados autofirmados en entornos de laboratorio
        )
        if resp.status_code == 200:
            event_id = resp.json().get("Event", {}).get("id", "")
            print(f"[+] Evento MISP creado: {event_id}")
            return event_id
        else:
            print(f"[-] Error MISP: {resp.status_code} - {resp.text[:200]}")
            return None

    def push_to_taxii(self, stix_bundle, taxii_url, collection_id, username, password):
        """Enviar bundle STIX a una colección TAXII 2.1."""
        from taxii2client.v21 import Collection
        collection = Collection(
            f"{taxii_url}/collections/{collection_id}/",
            user=username, password=password,
        )
        response = collection.add_objects(stix_bundle.serialize())
        print(f"[+] TAXII: Bundle publicado, estado: {response.status}")
        return response

distributor = IOCDistributor()
distributor.push_to_misp(
    iocs,
    misp_url="https://misp.organization.com",
    misp_key="YOUR_MISP_API_KEY",
    event_info="Malware Campaign IOCs - 2025",
)

Criterios de Validación

  • IOCs extraídos correctamente de texto libre con soporte de refanging
  • El desarme produce indicadores seguros y no cliqueables
  • El bundle STIX 2.1 contiene patrones de indicadores válidos
  • IOCs distribuidos exitosamente a MISP y TAXII
  • La deduplicación previene indicadores duplicados
  • La lista blanca previene falsos positivos en dominios conocidos como seguros

Referencias

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,

Skills relacionados