tracking-threat-actor-infrastructure

mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026

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$npx skills install mukul975/Anthropic-Cybersecurity-Skills/tracking-threat-actor-infrastructure
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summary

Threat actor infrastructure tracking involves monitoring and mapping adversary-controlled assets including command-and-control (C2) servers, phishing domains, exploit kit hosts, bulletproof hosting, a

skill.md
name
tracking-threat-actor-infrastructure
description
Threat actor infrastructure tracking involves monitoring and mapping adversary-controlled assets including command-and-control (C2) servers, phishing domains, exploit kit hosts, bulletproof hosting, a
domain
cybersecurity
subdomain
threat-intelligence
tags
- threat-intelligence - cti - ioc - mitre-attack - stix - infrastructure-tracking - shodan - censys - passive-dns
version
'1.0'
author
mahipal
license
Apache-2.0
nist_csf
- ID.RA-01 - ID.RA-05 - DE.CM-01 - DE.AE-02

Tracking Threat Actor Infrastructure

Overview

Threat actor infrastructure tracking involves monitoring and mapping adversary-controlled assets including command-and-control (C2) servers, phishing domains, exploit kit hosts, bulletproof hosting, and staging servers. This skill covers using passive DNS, certificate transparency logs, Shodan/Censys scanning, WHOIS analysis, and network fingerprinting to discover, track, and pivot across threat actor infrastructure over time.

When to Use

  • When managing security operations that require tracking threat actor infrastructure
  • When improving security program maturity and operational processes
  • When establishing standardized procedures for security team workflows
  • When integrating threat intelligence or vulnerability data into operations

Prerequisites

  • Python 3.9+ with shodan, censys, requests, stix2 libraries
  • API keys: Shodan, Censys, VirusTotal, SecurityTrails, PassiveTotal
  • Understanding of DNS, TLS/SSL certificates, IP allocation, ASN structure
  • Familiarity with passive DNS and certificate transparency concepts
  • Access to domain registration (WHOIS) lookup services

Key Concepts

Infrastructure Pivoting

Pivoting is the technique of using one known indicator to discover related infrastructure. Starting from a known C2 IP address, analysts can pivot via: passive DNS (find domains), reverse WHOIS (find related registrations), SSL certificates (find shared certs), SSH key fingerprints, HTTP response fingerprints, JARM/JA3S hashes, and WHOIS registrant data.

Passive DNS

Passive DNS databases record DNS query/response data observed at recursive resolvers. This allows analysts to find historical domain-to-IP mappings, discover domains hosted on a known C2 IP, and identify fast-flux or domain generation algorithm (DGA) behavior.

Certificate Transparency

Certificate Transparency (CT) logs publicly record all SSL/TLS certificates issued by CAs. Monitoring CT logs reveals new certificates registered for suspicious domains, helping identify phishing sites and C2 infrastructure before they become active.

Network Fingerprinting

  • JARM: Active TLS server fingerprint (hash of TLS handshake responses)
  • JA3S: Passive TLS server fingerprint (hash of Server Hello)
  • HTTP Headers: Server banners, custom headers, response patterns
  • Favicon Hash: Hash of HTTP favicon for server identification

Workflow

Step 1: Shodan Infrastructure Discovery

import shodan

api = shodan.Shodan("YOUR_SHODAN_API_KEY")

def discover_infrastructure(ip_address):
    """Discover services and metadata for a target IP."""
    try:
        host = api.host(ip_address)
        return {
            "ip": host["ip_str"],
            "org": host.get("org", ""),
            "asn": host.get("asn", ""),
            "isp": host.get("isp", ""),
            "country": host.get("country_name", ""),
            "city": host.get("city", ""),
            "os": host.get("os"),
            "ports": host.get("ports", []),
            "vulns": host.get("vulns", []),
            "hostnames": host.get("hostnames", []),
            "domains": host.get("domains", []),
            "tags": host.get("tags", []),
            "services": [
                {
                    "port": svc.get("port"),
                    "transport": svc.get("transport"),
                    "product": svc.get("product", ""),
                    "version": svc.get("version", ""),
                    "ssl_cert": svc.get("ssl", {}).get("cert", {}).get("subject", {}),
                    "jarm": svc.get("ssl", {}).get("jarm", ""),
                }
                for svc in host.get("data", [])
            ],
        }
    except shodan.APIError as e:
        print(f"[-] Shodan error: {e}")
        return None

def search_c2_framework(framework_name):
    """Search Shodan for known C2 framework signatures."""
    c2_queries = {
        "cobalt-strike": 'product:"Cobalt Strike Beacon"',
        "metasploit": 'product:"Metasploit"',
        "covenant": 'http.html:"Covenant" http.title:"Covenant"',
        "sliver": 'ssl.cert.subject.cn:"multiplayer" ssl.cert.issuer.cn:"operators"',
        "havoc": 'http.html_hash:-1472705893',
    }

    query = c2_queries.get(framework_name.lower(), framework_name)
    results = api.search(query, limit=100)

    hosts = []
    for match in results.get("matches", []):
        hosts.append({
            "ip": match["ip_str"],
            "port": match["port"],
            "org": match.get("org", ""),
            "country": match.get("location", {}).get("country_name", ""),
            "asn": match.get("asn", ""),
            "timestamp": match.get("timestamp", ""),
        })

    return hosts

Step 2: Passive DNS Pivoting

import requests

def passive_dns_lookup(indicator, api_key, indicator_type="ip"):
    """Query SecurityTrails for passive DNS records."""
    base_url = "https://api.securitytrails.com/v1"
    headers = {"APIKEY": api_key, "Accept": "application/json"}

    if indicator_type == "ip":
        url = f"{base_url}/search/list"
        payload = {
            "filter": {"ipv4": indicator}
        }
        resp = requests.post(url, json=payload, headers=headers, timeout=30)
    else:
        url = f"{base_url}/domain/{indicator}/subdomains"
        resp = requests.get(url, headers=headers, timeout=30)

    if resp.status_code == 200:
        return resp.json()
    return None


def query_passive_total(indicator, user, api_key):
    """Query PassiveTotal for passive DNS and WHOIS data."""
    base_url = "https://api.passivetotal.org/v2"
    auth = (user, api_key)

    # Passive DNS
    pdns_resp = requests.get(
        f"{base_url}/dns/passive",
        params={"query": indicator},
        auth=auth,
        timeout=30,
    )

    # WHOIS
    whois_resp = requests.get(
        f"{base_url}/whois",
        params={"query": indicator},
        auth=auth,
        timeout=30,
    )

    results = {}
    if pdns_resp.status_code == 200:
        results["passive_dns"] = pdns_resp.json().get("results", [])
    if whois_resp.status_code == 200:
        results["whois"] = whois_resp.json()

    return results

Step 3: Certificate Transparency Monitoring

import requests

def search_ct_logs(domain):
    """Search Certificate Transparency logs via crt.sh."""
    resp = requests.get(
        f"https://crt.sh/?q=%.{domain}&output=json",
        timeout=30,
    )

    if resp.status_code == 200:
        certs = resp.json()
        unique_domains = set()
        cert_info = []

        for cert in certs:
            name_value = cert.get("name_value", "")
            for name in name_value.split("\n"):
                unique_domains.add(name.strip())

            cert_info.append({
                "id": cert.get("id"),
                "issuer": cert.get("issuer_name", ""),
                "common_name": cert.get("common_name", ""),
                "name_value": name_value,
                "not_before": cert.get("not_before", ""),
                "not_after": cert.get("not_after", ""),
                "serial_number": cert.get("serial_number", ""),
            })

        return {
            "domain": domain,
            "total_certificates": len(certs),
            "unique_domains": sorted(unique_domains),
            "certificates": cert_info[:50],
        }
    return None


def monitor_new_certs(domains, interval_hours=1):
    """Monitor for newly issued certificates for a list of domains."""
    from datetime import datetime, timedelta

    cutoff = (datetime.utcnow() - timedelta(hours=interval_hours)).isoformat()
    new_certs = []

    for domain in domains:
        result = search_ct_logs(domain)
        if result:
            for cert in result.get("certificates", []):
                if cert.get("not_before", "") > cutoff:
                    new_certs.append({
                        "domain": domain,
                        "cert": cert,
                    })

    return new_certs

Step 4: Infrastructure Correlation and Timeline

from datetime import datetime

def build_infrastructure_timeline(indicators):
    """Build a timeline of infrastructure changes."""
    timeline = []

    for ind in indicators:
        if "passive_dns" in ind:
            for record in ind["passive_dns"]:
                timeline.append({
                    "timestamp": record.get("firstSeen", ""),
                    "event": "dns_resolution",
                    "source": record.get("resolve", ""),
                    "target": record.get("value", ""),
                    "record_type": record.get("recordType", ""),
                })

        if "certificates" in ind:
            for cert in ind["certificates"]:
                timeline.append({
                    "timestamp": cert.get("not_before", ""),
                    "event": "certificate_issued",
                    "domain": cert.get("common_name", ""),
                    "issuer": cert.get("issuer", ""),
                })

    timeline.sort(key=lambda x: x.get("timestamp", ""))
    return timeline

Validation Criteria

  • Shodan/Censys queries return infrastructure details for target IPs
  • Passive DNS reveals historical domain-IP mappings
  • Certificate transparency search finds associated domains
  • Infrastructure pivoting discovers new related indicators
  • Timeline shows infrastructure evolution over time
  • Results are exportable as STIX 2.1 Infrastructure objects

References

how to use tracking-threat-actor-infrastructure

How to use tracking-threat-actor-infrastructure on Cursor

AI-first code editor with Composer

1

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 tracking-threat-actor-infrastructure
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install mukul975/Anthropic-Cybersecurity-Skills/tracking-threat-actor-infrastructure

The skills CLI fetches tracking-threat-actor-infrastructure from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/tracking-threat-actor-infrastructure

Reload or restart Cursor to activate tracking-threat-actor-infrastructure. Access the skill through slash commands (e.g., /tracking-threat-actor-infrastructure) 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. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.853 reviews
  • Aanya Chen· Dec 28, 2024

    I recommend tracking-threat-actor-infrastructure for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Chaitanya Patil· Dec 24, 2024

    Useful defaults in tracking-threat-actor-infrastructure — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Aanya Garcia· Dec 24, 2024

    Keeps context tight: tracking-threat-actor-infrastructure is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Camila Agarwal· Dec 12, 2024

    tracking-threat-actor-infrastructure is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Carlos Harris· Nov 27, 2024

    tracking-threat-actor-infrastructure has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Henry Shah· Nov 19, 2024

    Keeps context tight: tracking-threat-actor-infrastructure is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Piyush G· Nov 15, 2024

    tracking-threat-actor-infrastructure is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Valentina Diallo· Nov 15, 2024

    I recommend tracking-threat-actor-infrastructure for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Min Singh· Nov 3, 2024

    Useful defaults in tracking-threat-actor-infrastructure — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Ishan Robinson· Oct 22, 2024

    I recommend tracking-threat-actor-infrastructure for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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