/building-ioc-defanging-and-sharing-pipeline
Build an automated pipeline that ingests raw IOCs (URLs, IPs, domains,
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/building-ioc-defanging-and-sharing-pipeline
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Build an automated pipeline that ingests raw IOCs (URLs, IPs, domains,
SKILL.md
building-ioc-defanging-and-sharing-pipeline.SKILL.mdname: building-ioc-defanging-and-sharing-pipeline
description: Build an automated pipeline that ingests raw IOCs (URLs, IPs, domains,
emails), normalizes and deduplicates them, then produces defanged renderings for
safe human reading alongside canonical STIX 2.1 bundles distributed via TAXII servers,
MISP, or email reports. Use when preparing indicators of compromise for safe analyst
sharing or automating threat intel distribution to TAXII/MISP feeds.
domain: cybersecurity
subdomain: threat-intelligence
tags:
- ioc
- defanging
- threat-sharing
- stix
- pipeline
- indicator
- automation
- threat-intelligence
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- ID.RA-01
- ID.RA-05
- DE.CM-01
- DE.AE-02
mitre_attack:
- T1071.001
- T1583.001
- T1105
- T1566.002
Building IOC Defanging and Sharing Pipeline
Overview
IOC defanging modifies potentially malicious indicators (URLs, IP addresses, domains, email addresses) to prevent accidental clicks or execution while preserving readability for analysis and sharing. This skill covers building an automated pipeline that ingests raw IOCs from multiple sources, normalizes and deduplicates them, applies defanging for safe human consumption, converts them to STIX 2.1 format for machine consumption, and distributes through TAXII servers, MISP instances, and email reports.
When to Use
- When deploying or configuring building ioc defanging and sharing pipeline capabilities in your environment
- When establishing security controls aligned to compliance requirements
- When building or improving security architecture for this domain
- When conducting security assessments that require this implementation
Prerequisites
- Python 3.9+ with `defang`, `ioc-fanger`, `stix2`, `requests`, `validators` libraries
- MISP instance or TAXII server for automated sharing
- Understanding of IOC types: IPv4/IPv6, domains, URLs, email addresses, file hashes
- Familiarity with STIX 2.1 Indicator patterns and TLP marking definitions
- Access to threat intelligence feeds for IOC ingestion
Key Concepts
IOC Defanging Standards
Defanging replaces active protocol and domain components to prevent execution: `http://` becomes `hxxp://`, `https://` becomes `hxxps://`, dots in domains/IPs become `[.]`, `@` in emails becomes `[@]`. This is critical for sharing IOCs in reports, emails, Slack channels, and paste sites where auto-linking could trigger network connections to malicious infrastructure.
IOC Normalization
Raw IOCs from different sources come in inconsistent formats. Normalization involves converting to lowercase, removing trailing slashes and whitespace, extracting domains from URLs, resolving URL encoding, validating format correctness, and deduplicating across sources.
STIX 2.1 Indicator Patterns
STIX patterns express IOCs in a standardized format: `[ipv4-addr:value = '203.0.113.1']`, `[domain-name:value = 'malicious.example.com']`, `[url:value = 'http://evil.com/payload']`, `[file:hashes.'SHA-256' = 'abc123...']`. Each indicator includes valid_from, indicator_types, confidence, and optional TLP markings.
Workflow
Step 1: Build IOC Extraction and Normalization
import re
import hashlib
from urllib.parse import urlparse, unquote
from datetime import datetime
class IOCExtractor:
"""Extract and normalize IOCs from text."""
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):
"""Extract all IOC types from free text."""
# Refang any already-defanged indicators first
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)
# Remove domains that are part of 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"[+] Extracted {total} unique IOCs from text")
return {k: sorted(v) for k, v in iocs.items()}
def _refang(self, text):
"""Convert defanged indicators back to active form."""
text = text.replace("hxxp://", "http://").replace("hxxps://", "https://")
text = text.replace("[.]", ".").replace("[@]", "@")
text = text.replace("[://]", "://").replace("(.)", ".")
return text
def _normalize(self, value, ioc_type):
"""Normalize an IOC value."""
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):
"""Check if IOC is in whitelist."""
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 = IOCExtrRead more
name: building-ioc-defanging-and-sharing-pipeline description: Build an automated pipeline that ingests raw IOCs (URLs, IPs, domains, emails), normalizes and deduplicates them, then produces defanged renderings for safe human reading alongside canonical STIX 2.1 bundles distributed via TAXII servers, MISP, or email reports. Use when preparing indicators of compromise for safe analyst sharing or automating threat intel distribution to TAXII/MISP feeds. domain: cybersecurity subdomain: threat-intelligence tags: - ioc - defanging - threat-sharing - stix - pipeline - indicator - automation - threat-intelligence version: '1.0' author: mahipal license: Apache-2.0 nist_csf: - ID.RA-01 - ID.RA-05 - DE.CM-01 - DE.AE-02 mitre_attack: - T1071.001 - T1583.001 - T1105 - T1566.002
Building IOC Defanging and Sharing Pipeline
Overview
IOC defanging modifies potentially malicious indicators (URLs, IP addresses, domains, email addresses) to prevent accidental clicks or execution while preserving readability for analysis and sharing. This skill covers building an automated pipeline that ingests raw IOCs from multiple sources, normalizes and deduplicates them, applies defanging for safe human consumption, converts them to STIX 2.1 format for machine consumption, and distributes through TAXII servers, MISP instances, and email reports.
When to Use
- When deploying or configuring building ioc defanging and sharing pipeline capabilities in your environment
- When establishing security controls aligned to compliance requirements
- When building or improving security architecture for this domain
- When conducting security assessments that require this implementation
Prerequisites
- Python 3.9+ with `defang`, `ioc-fanger`, `stix2`, `requests`, `validators` libraries
- MISP instance or TAXII server for automated sharing
- Understanding of IOC types: IPv4/IPv6, domains, URLs, email addresses, file hashes
- Familiarity with STIX 2.1 Indicator patterns and TLP marking definitions
- Access to threat intelligence feeds for IOC ingestion
Key Concepts
IOC Defanging Standards
Defanging replaces active protocol and domain components to prevent execution: `http://` becomes `hxxp://`, `https://` becomes `hxxps://`, dots in domains/IPs become `[.]`, `@` in emails becomes `[@]`. This is critical for sharing IOCs in reports, emails, Slack channels, and paste sites where auto-linking could trigger network connections to malicious infrastructure.
IOC Normalization
Raw IOCs from different sources come in inconsistent formats. Normalization involves converting to lowercase, removing trailing slashes and whitespace, extracting domains from URLs, resolving URL encoding, validating format correctness, and deduplicating across sources.
STIX 2.1 Indicator Patterns
STIX patterns express IOCs in a standardized format: `[ipv4-addr:value = '203.0.113.1']`, `[domain-name:value = 'malicious.example.com']`, `[url:value = 'http://evil.com/payload']`, `[file:hashes.'SHA-256' = 'abc123...']`. Each indicator includes valid_from, indicator_types, confidence, and optional TLP markings.
Workflow
Step 1: Build IOC Extraction and Normalization
import re
import hashlib
from urllib.parse import urlparse, unquote
from datetime import datetime
class IOCExtractor:
"""Extract and normalize IOCs from text."""
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):
"""Extract all IOC types from free text."""
# Refang any already-defanged indicators first
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)
# Remove domains that are part of 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"[+] Extracted {total} unique IOCs from text")
return {k: sorted(v) for k, v in iocs.items()}
def _refang(self, text):
"""Convert defanged indicators back to active form."""
text = text.replace("hxxp://", "http://").replace("hxxps://", "https://")
text = text.replace("[.]", ".").replace("[@]", "@")
text = text.replace("[://]", "://").replace("(.)", ".")
return text
def _normalize(self, value, ioc_type):
"""Normalize an IOC value."""
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):
"""Check if IOC is in whitelist."""
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 = IOCExtr817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains · Apache 2.0
Repo: mukul975/Anthropic-Cybersecurity-Skills
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