Dark web monitoring involves systematically scanning Tor hidden services, underground forums, paste sites, and dark web marketplaces to identify threats targeting an organization, including leaked cre
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
Instruções da origem · Visualização somente leitura
name
performing-dark-web-monitoring-for-threats
description
Dark web monitoring involves systematically scanning Tor hidden services, underground forums, paste sites, and dark web marketplaces to identify threats targeting an organization, including leaked cre
Dark web monitoring involves systematically scanning Tor hidden services, underground forums, paste sites, and dark web marketplaces to identify threats targeting an organization, including leaked credentials, data breaches, threat actor discussions, vulnerability exploitation tools, and planned attacks. This skill covers setting up monitoring infrastructure, using Tor-based collection tools, implementing automated alerting for brand mentions and credential leaks, and analyzing dark web intelligence for actionable threat indicators.
When to Use
When conducting security assessments that involve performing dark web monitoring for threats
When following incident response procedures for related security events
When performing scheduled security testing or auditing activities
When validating security controls through hands-on testing
Detection Gaps & Validation
Source coverage gaps: clearnet aggregators (HIBP, Ransomwatch) only cover known breaches and publicly posted ransomware victims - invite-only forums, vetted marketplaces, and private Telegram/Discord channels need aged personas and seller vetting you will not get from automated crawling. Absence of a hit is not evidence of safety.
Volatility:.onion paste and leak sites are ephemeral (seized, rotated, or DDoSed within days); cache content at collection time because the URL may be dead before an analyst reviews it.
Language/obfuscation: actors post in Russian, Farsi, slang, and leetspeak, so naive English keyword matching misses leaks. Normalize/translate and match on stable selectors (corporate email domains, internal project codenames) rather than the brand name alone.
How to confirm a leak: validate before raising severity - combolist recycling is rampant, so the same user:pass reappears across dozens of "new" leaks; cross-check against prior dumps, confirm the domain actually belongs to you, and where lawful test whether the password is still current. Treat a ransomware leak-site listing as confirmed only after matching victim name plus a known-internal artifact, not a partial name collision.
OPSEC caveat: never authenticate to or download from a paste/onion source from an attributable host while validating.
Prerequisites
Tor Browser and Tor proxy (SOCKS5 on port 9050)
Python 3.9+ with requests, stem, beautifulsoup4, stix2 libraries
Understanding of Tor hidden service architecture (.onion domains)
API access to dark web monitoring services (Flare, SpyCloud, DarkOwl, Intel 471)
Awareness of legal and ethical boundaries for dark web research
Isolated VM for dark web browsing (no personal or corporate identity leakage)
Key Concepts
Dark Web Intelligence Sources
Underground Forums: Hacking forums where threat actors discuss TTPs, sell exploits, and share tools
Paste Sites: Platforms for sharing stolen data, credentials, and code snippets
Marketplaces: Dark web markets selling stolen data, RaaS, exploit kits, and access
Telegram/Discord: Alternative communication channels for cybercriminal groups
Ransomware Leak Sites: Blogs where ransomware groups post stolen data from victims
Collection Methods
Automated Crawling: Tor-based web crawlers scanning hidden services
API-Based Monitoring: Commercial dark web monitoring APIs (Flare, DarkOwl, Intel 471)
Manual HUMINT: Analyst-driven research on specific forums and marketplaces
Credential Monitoring: Breach databases and paste site monitoring for leaked credentials
OPSEC for Dark Web Research
Use dedicated VMs with no personal data
Route all traffic through Tor (Whonix or Tails recommended)
Never use personal accounts or identifiable information
Use separate email addresses and personas for forum registration
Disable JavaScript in Tor Browser for enhanced security
Never download or execute files from dark web sources on production systems
Workflow
Step 1: Set Up Tor-Based HTTP Client
import requests
from requests.adapters import HTTPAdapter
defcreate_tor_session():
"""Create a requests session routed through Tor SOCKS5 proxy."""
session = requests.Session()
session.proxies = {
"http": "socks5h://127.0.0.1:9050",
"https": "socks5h://127.0.0.1:9050",
}
session.headers.update({
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; rv:109.0) Gecko/20100101 Firefox/115.0",
})
return session
defverify_tor_connection(session):
"""Verify that traffic is routed through Tor."""try:
resp = session.get("https://check.torproject.org/api/ip", timeout=30)
data = resp.json()
return {
"is_tor": data.get("IsTor", False),
"ip": data.get("IP", ""),
}
except Exception as e:
return {"error": str(e)}
Step 2: Monitor Paste Sites for Credential Leaks
import re
from datetime import datetime
defmonitor_paste_sites(session, organization_domains):
"""Monitor paste sites for leaked credentials matching organization domains."""
findings = []
# Check Have I Been Pwned API (clearnet)for domain in organization_domains:
try:
resp = requests.get(
f"https://haveibeenpwned.com/api/v3/breaches",
headers={"hibp-api-key": "YOUR_HIBP_KEY"},
timeout=30,
)
if resp.status_code == 200:
breaches = resp.json()
for breach in breaches:
if domain.lower() in breach.get("Domain", "").lower():
findings.append({
"source": "HIBP",
"breach_name": breach["Name"],
"breach_date": breach.get("BreachDate"),
"data_classes": breach.get("DataClasses", []),
"pwn_count": breach.get("PwnCount", 0),
"domain": domain,
})
except Exception as e:
print(f"[-] HIBP error for {domain}: {e}")
return findings
defsearch_for_keywords(session, keywords, onion_paste_urls):
"""Search dark web paste sites for specific keywords."""
results = []
for paste_url in onion_paste_urls:
try:
resp = session.get(paste_url, timeout=60)
if resp.status_code == 200:
content = resp.text.lower()
for keyword in keywords:
if keyword.lower() in content:
results.append({
"url": paste_url,
"keyword": keyword,
"timestamp": datetime.utcnow().isoformat(),
"snippet": extract_context(content, keyword.lower()),
})
except Exception as e:
print(f"[-] Error fetching {paste_url}: {e}")
return results
defextract_context(text, keyword, context_chars=200):
"""Extract text context around a keyword match."""
idx = text.find(keyword)
if idx == -1:
return""
start = max(0, idx - context_chars)
end = min(len(text), idx + len(keyword) + context_chars)
return text[start:end]
Step 3: Monitor Ransomware Leak Sites
defcheck_ransomware_leak_sites(session, organization_name):
"""Check known ransomware group leak sites for organization mentions."""# Use Ransomwatch API (clearnet aggregator of ransomware leak sites)try:
resp = requests.get(
"https://raw.githubusercontent.com/joshhighet/ransomwatch/main/posts.json",
timeout=30,
)
if resp.status_code == 200:
posts = resp.json()
matches = []
for post in posts:
post_title = post.get("post_title", "").lower()
if organization_name.lower() in post_title:
matches.append({
"group": post.get("group_name", ""),
"title": post.get("post_title", ""),
"discovered": post.get("discovered", ""),
"url": post.get("post_url", ""),
})
return matches
except Exception as e:
print(f"[-] Ransomwatch error: {e}")
return []
Step 4: Generate Dark Web Intelligence Report
defgenerate_dark_web_report(findings, organization):
"""Generate structured dark web intelligence report."""
report = {
"organization": organization,
"report_date": datetime.utcnow().isoformat(),
"executive_summary": "",
"credential_leaks": [],
"ransomware_mentions": [],
"dark_web_mentions": [],
"recommendations": [],
}
for finding in findings:
if finding.get("source") == "HIBP":
report["credential_leaks"].append(finding)
elif finding.get("group"):
report["ransomware_mentions"].append(finding)
else:
report["dark_web_mentions"].append(finding)
# Generate executive summary
cred_count = len(report["credential_leaks"])
ransom_count = len(report["ransomware_mentions"])
report["executive_summary"] = (
f"Monitoring identified {cred_count} credential leak sources "f"and {ransom_count} ransomware group mentions for {organization}."
)
if ransom_count > 0:
report["recommendations"].append(
"CRITICAL: Organization mentioned on ransomware leak site. ""Initiate incident response immediately."
)
if cred_count > 0:
report["recommendations"].append(
"HIGH: Leaked credentials detected. Force password resets for ""affected accounts and enable MFA."
)
return report
Validation Criteria
Tor connection established and verified via check.torproject.org
Credential leak monitoring returns results from HIBP and paste sites
Ransomware leak site monitoring identifies relevant mentions
Dark web intelligence report generated with actionable recommendations
All monitoring performed within legal and ethical boundaries
OPSEC maintained: no personal or corporate identity exposure
