building-ioc-defanging-and-sharing-pipeline▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Build an automated pipeline to defang indicators of compromise (URLs, IPs, domains, emails) for safe sharing and distribute them in STIX format through TAXII feeds and threat intelligence platforms.
| name | building-ioc-defanging-and-sharing-pipeline |
| description | Build an automated pipeline to defang indicators of compromise (URLs, IPs, domains, emails) for safe sharing and distribute them in STIX format through TAXII feeds and threat intelligence platforms. |
| 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 |
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,validatorslibraries - 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 = 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)
Step 2: Defanging Engine
class IOCDefanger:
"""Defang IOCs for safe sharing in reports and communications."""
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):
"""Defang all IOCs in a dictionary."""
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 # Hashes don't need defanging
return defanged
def generate_sharing_report(self, iocs, defanged, report_name="IOC Report"):
"""Generate a human-readable defanged IOC report."""
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)
Step 3: Convert to STIX 2.1 Format
from stix2 import Indicator, Bundle, TLP_WHITE, TLP_GREEN, TLP_AMBER
from datetime import datetime
class STIXConverter:
"""Convert raw IOCs to STIX 2.1 Indicator objects."""
TLP_MAP = {"white": TLP_WHITE, "green": TLP_GREEN, "amber": TLP_AMBER}
def iocs_to_stix(self, iocs, tlp="green", confidence=75):
"""Convert IOC dictionary to STIX 2.1 bundle."""
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"[+] Created STIX bundle with {len(stix_objects)} indicators")
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))
Step 4: Distribute Through MISP and TAXII
import requests
import json
class IOCDistributor:
"""Distribute IOCs through various channels."""
def push_to_misp(self, iocs, misp_url, misp_key, event_info):
"""Push IOCs to MISP as a new event."""
headers = {
"Authorization": misp_key,
"Content-Type": "application/json",
"Accept": "application/json",
}
event = {
"Event": {
"info": event_info,
"distribution": "1", # This community only
"threat_level_id": "2", # Medium
"analysis": "2", # Completed
"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", # Set SKIP_TLS_VERIFY=true for self-signed certs in lab environments
)
if resp.status_code == 200:
event_id = resp.json().get("Event", {}).get("id", "")
print(f"[+] MISP event created: {event_id}")
return event_id
else:
print(f"[-] MISP error: {resp.status_code} - {resp.text[:200]}")
return None
def push_to_taxii(self, stix_bundle, taxii_url, collection_id, username, password):
"""Push STIX bundle to TAXII 2.1 collection."""
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: Published bundle, status: {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",
)
Validation Criteria
- IOCs extracted correctly from free text with refanging support
- Defanging produces safe, non-clickable indicators
- STIX 2.1 bundle contains valid indicator patterns
- IOCs distributed to MISP and TAXII successfully
- Deduplication prevents duplicate indicators
- Whitelisting prevents false positives on known-good domains
References
How to use building-ioc-defanging-and-sharing-pipeline on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add building-ioc-defanging-and-sharing-pipeline
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches building-ioc-defanging-and-sharing-pipeline from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate building-ioc-defanging-and-sharing-pipeline. Access the skill through slash commands (e.g., /building-ioc-defanging-and-sharing-pipeline) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
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Ratings
4.7★★★★★43 reviews- ★★★★★Nia Martinez· Dec 28, 2024
Solid pick for teams standardizing on skills: building-ioc-defanging-and-sharing-pipeline is focused, and the summary matches what you get after install.
- ★★★★★Shikha Mishra· Dec 20, 2024
Solid pick for teams standardizing on skills: building-ioc-defanging-and-sharing-pipeline is focused, and the summary matches what you get after install.
- ★★★★★Advait Yang· Dec 8, 2024
I recommend building-ioc-defanging-and-sharing-pipeline for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Advait Torres· Dec 4, 2024
building-ioc-defanging-and-sharing-pipeline is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Zara Choi· Nov 23, 2024
building-ioc-defanging-and-sharing-pipeline reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Kofi Reddy· Nov 19, 2024
We added building-ioc-defanging-and-sharing-pipeline from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Yash Thakker· Nov 11, 2024
We added building-ioc-defanging-and-sharing-pipeline from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Tariq Thomas· Oct 14, 2024
Registry listing for building-ioc-defanging-and-sharing-pipeline matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Maya Singh· Oct 10, 2024
building-ioc-defanging-and-sharing-pipeline fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Dhruvi Jain· Oct 2, 2024
building-ioc-defanging-and-sharing-pipeline fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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