Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
mukul975 avatar

Analyzing Malware Family Relationships With Malpedia

mukul975/anthropic-cybersecurity-skills

  • 274 installs
  • 27.3k repo stars
  • Updated August 2, 2026
  • mukul975/anthropic-cybersecurity-skills

A security analyst or malware researcher uses this skill to understand relationships between malware families and track their evolution for threat intelligence.

About

This skill teaches how to analyze and map malware family relationships using Malpedia, a collaborative repository of malware intelligence. Security researchers and analysts use it to understand how different malware strains are connected—whether through shared code, authors, or tactics—which is critical for building comprehensive threat profiles. By mapping these relationships, teams can predict attack patterns, detect new variants faster, and respond more effectively to security incidents.

  • Track malware family lineages and evolutionary relationships
  • Identify shared code, techniques, and infrastructure across malware variants
  • Build threat intelligence models for better incident response

Analyzing Malware Family Relationships With Malpedia by the numbers

  • 274 all-time installs (skills.sh)
  • +17 installs in the week ending Aug 4, 2026 (Skillselion tracking)
  • Ranked #648 of 2,203 Security skills by installs in the Skillselion catalog
  • Security screen: HIGH risk (skills.sh audit)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill analyzing-malware-family-relationships-with-malpedia

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs274
repo stars★ 27.3k
Security audit2 / 3 scanners passed
Last updatedAugust 2, 2026
Repositorymukul975/anthropic-cybersecurity-skills ↗

What it does

A security analyst or malware researcher uses this skill to understand relationships between malware families and track their evolution for threat intelligence.

Files

SKILL.mdMarkdownGitHub ↗

Analyzing Malware Family Relationships with Malpedia

Overview

Malpedia is a collaborative platform maintained by Fraunhofer FKIE that catalogs malware families with their aliases, YARA rules, threat actor associations, and reference reports. With over 2,600 malware families documented, it serves as the definitive resource for understanding malware lineages, tracking variant evolution, and linking malware to specific threat groups. This skill covers querying the Malpedia API, mapping malware family relationships, extracting YARA rules for detection, and building intelligence on malware ecosystems used by adversaries.

When to Use

  • When investigating security incidents that require analyzing malware family relationships with malpedia
  • When building detection rules or threat hunting queries for this domain
  • When SOC analysts need structured procedures for this analysis type
  • When validating security monitoring coverage for related attack techniques

Prerequisites

  • Python 3.9+ with requests, yara-python, stix2 libraries
  • Malpedia API key (register at https://malpedia.caad.fkie.fraunhofer.de/)
  • Understanding of malware classification and naming conventions
  • Familiarity with YARA rule syntax for detection
  • Access to malware samples for validation (optional)

Key Concepts

Malpedia Data Model

Malpedia organizes malware into Families (e.g., "win.cobalt_strike"), each containing: aliases (vendor-specific names like "Beacon", "CobaltStrike"), YARA rules (community and vendor-contributed), actor associations (threat groups using the family), reference reports (CTI reports documenting the family), and sample hashes (representative samples for each variant).

Malware Family Naming

Malpedia uses the format platform.family_name (e.g., win.emotet, elf.mirai, apk.flubot). Platforms include win (Windows), elf (Linux), apk (Android), osx (macOS), and py (Python). This standardized naming resolves the "many names" problem where different vendors assign different names to the same malware.

Family Relationships

Malware families have relationships including: parent-child (code reuse, forks), loader-payload (Emotet loads TrickBot loads Ryuk), shared authorship (same threat actor develops multiple tools), and infrastructure sharing (common C2 frameworks).

Workflow

Step 1: Query Malpedia API for Malware Families

import requests
import json
from collections import defaultdict

class MalpediaClient:
    BASE_URL = "https://malpedia.caad.fkie.fraunhofer.de/api"

    def __init__(self, api_key):
        self.headers = {"Authorization": f"apitoken {api_key}"}

    def get_family_list(self):
        """Get list of all malware families."""
        resp = requests.get(f"{self.BASE_URL}/list/families",
                           headers=self.headers, timeout=30)
        if resp.status_code == 200:
            families = resp.json()
            print(f"[+] Malpedia: {len(families)} malware families")
            return families
        return {}

    def get_family_info(self, family_name):
        """Get detailed information about a malware family."""
        resp = requests.get(f"{self.BASE_URL}/get/family/{family_name}",
                           headers=self.headers, timeout=30)
        if resp.status_code == 200:
            info = resp.json()
            print(f"[+] Family: {family_name}")
            print(f"    Aliases: {info.get('alt_names', [])}")
            print(f"    Actors: {[a.get('value', '') for a in info.get('attribution', [])]}")
            print(f"    URLs: {len(info.get('urls', []))} references")
            return info
        print(f"[-] Family not found: {family_name}")
        return None

    def get_family_yara(self, family_name):
        """Get YARA rules for a malware family."""
        resp = requests.get(f"{self.BASE_URL}/get/yara/{family_name}",
                           headers=self.headers, timeout=30)
        if resp.status_code == 200:
            rules = resp.json()
            rule_count = sum(len(v) for v in rules.values()) if isinstance(rules, dict) else 0
            print(f"[+] YARA rules for {family_name}: {rule_count} rules")
            return rules
        return {}

    def get_actor_families(self, actor_name):
        """Get malware families associated with a threat actor."""
        resp = requests.get(f"{self.BASE_URL}/get/actor/{actor_name}",
                           headers=self.headers, timeout=30)
        if resp.status_code == 200:
            data = resp.json()
            families = data.get("families", {})
            print(f"[+] {actor_name}: {len(families)} malware families")
            return data
        return {}

    def search_families(self, keyword):
        """Search families by keyword."""
        all_families = self.get_family_list()
        matches = {
            name: info for name, info in all_families.items()
            if keyword.lower() in name.lower()
            or keyword.lower() in str(info.get("alt_names", [])).lower()
        }
        print(f"[+] Search '{keyword}': {len(matches)} matches")
        return matches

client = MalpediaClient("YOUR_MALPEDIA_API_KEY")
families = client.get_family_list()
emotet_info = client.get_family_info("win.emotet")

Step 2: Map Malware Family Relationships

class MalwareFamilyMapper:
    def __init__(self, malpedia_client):
        self.client = malpedia_client
        self.relationship_graph = defaultdict(list)

    def map_actor_ecosystem(self, actor_name):
        """Map the malware ecosystem used by a threat actor."""
        actor_data = self.client.get_actor_families(actor_name)
        families = actor_data.get("families", {})

        ecosystem = {
            "actor": actor_name,
            "families": [],
            "family_count": len(families),
        }

        for family_name in families:
            info = self.client.get_family_info(family_name)
            if info:
                ecosystem["families"].append({
                    "name": family_name,
                    "aliases": info.get("alt_names", []),
                    "description": info.get("description", "")[:200],
                    "shared_actors": [
                        a.get("value", "")
                        for a in info.get("attribution", [])
                    ],
                    "reference_count": len(info.get("urls", [])),
                })

        print(f"\n=== {actor_name} Malware Ecosystem ===")
        for fam in ecosystem["families"]:
            shared = [a for a in fam["shared_actors"] if a != actor_name]
            print(f"  {fam['name']}")
            print(f"    Aliases: {fam['aliases'][:5]}")
            if shared:
                print(f"    Also used by: {shared}")

        return ecosystem

    def find_shared_tooling(self, actor_names):
        """Find malware families shared between threat actors."""
        actor_families = {}
        for actor in actor_names:
            data = self.client.get_actor_families(actor)
            actor_families[actor] = set(data.get("families", {}).keys())

        # Find overlaps
        shared = {}
        for i, actor1 in enumerate(actor_names):
            for actor2 in actor_names[i+1:]:
                common = actor_families[actor1] & actor_families[actor2]
                if common:
                    shared[f"{actor1} <-> {actor2}"] = sorted(common)

        print(f"\n=== Shared Tooling Analysis ===")
        for pair, families in shared.items():
            print(f"  {pair}: {len(families)} shared families")
            for f in families[:5]:
                print(f"    - {f}")

        return shared

    def build_loader_payload_chain(self, family_name):
        """Build the loader-payload delivery chain for a family."""
        info = self.client.get_family_info(family_name)
        if not info:
            return {}

        chain = {
            "family": family_name,
            "description": info.get("description", ""),
            "known_loaders": [],
            "known_payloads": [],
        }

        # Common known delivery chains
        known_chains = {
            "win.emotet": {"loaders": ["email/macro"], "payloads": ["win.trickbot", "win.qakbot", "win.cobalt_strike"]},
            "win.trickbot": {"loaders": ["win.emotet"], "payloads": ["win.ryuk", "win.conti", "win.cobalt_strike"]},
            "win.qakbot": {"loaders": ["email/macro", "win.emotet"], "payloads": ["win.cobalt_strike", "win.blackbasta"]},
            "win.cobalt_strike": {"loaders": ["win.emotet", "win.trickbot", "win.qakbot"], "payloads": ["ransomware"]},
        }

        if family_name in known_chains:
            chain["known_loaders"] = known_chains[family_name]["loaders"]
            chain["known_payloads"] = known_chains[family_name]["payloads"]

        return chain

mapper = MalwareFamilyMapper(client)
ecosystem = mapper.map_actor_ecosystem("Wizard Spider")
shared = mapper.find_shared_tooling(["Wizard Spider", "FIN7", "Lazarus Group"])
chain = mapper.build_loader_payload_chain("win.emotet")

Step 3: Extract and Compile YARA Rules

def compile_yara_ruleset(client, family_names, output_file="malware_yara_rules.yar"):
    """Compile YARA rules for multiple malware families."""
    all_rules = []
    for family in family_names:
        yara_data = client.get_family_yara(family)
        if isinstance(yara_data, dict):
            for source, rules in yara_data.items():
                if isinstance(rules, list):
                    for rule in rules:
                        all_rules.append(f"// Source: {source} - Family: {family}\n{rule}")
                elif isinstance(rules, str):
                    all_rules.append(f"// Source: {source} - Family: {family}\n{rules}")

    with open(output_file, "w") as f:
        f.write(f"// Malpedia YARA Rules - {len(all_rules)} rules\n")
        f.write(f"// Families: {', '.join(family_names)}\n\n")
        for rule in all_rules:
            f.write(rule + "\n\n")

    print(f"[+] Compiled {len(all_rules)} YARA rules to {output_file}")
    return all_rules

compile_yara_ruleset(client, ["win.emotet", "win.trickbot", "win.cobalt_strike"])

Validation Criteria

  • Malpedia API queried successfully for malware families
  • Family information retrieved with aliases, actors, and references
  • Actor-family relationships mapped correctly
  • Shared tooling between actors identified
  • YARA rules extracted and compiled for detection
  • Loader-payload chains documented for threat intelligence

References

Related skills

microsoftEntra App RegistrationCorrectly register an application in Microsoft Entra ID, configure OAuth 2.0 flows, request the right API permissions, and generate working MSAL authentication snippets484k1.3kmicrosoftAzure ComplianceRun automated Azure compliance scans, security posture checks, and Key Vault expiration audits before deploying.484k1.3kxixu-meOpenclaw Secure Linux CloudDeploy and harden an OpenClaw agent instance on a Linux cloud server following battle-tested security defaults.270k72better-authBetter Auth Best PracticesCorrectly configure Better Auth for secure authentication with database adapters, sessions, OAuth, email/password, and plugins in TypeScript projects.80.9k206firebaseFirebase Security Rules AuditorAutomatically audit Firebase Firestore security rules for bypass vulnerabilities and logic gaps before deploying.80.4k397squirrelscanAudit WebsiteRun comprehensive audits that surface SEO, performance, security, technical, and content issues with LLM-optimized reports and health scores.65.6k85

FAQ

Is Analyzing Malware Family Relationships With Malpedia safe to install?

skills.sh reports 2 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

Securityauditcompliance
Browse by category
AI & AgentsFrontend & DesignBackend & DataDev ToolsAutomationTesting & ReviewSecurityMonitoring & CloudDocs & Planning
Design & frontend
Design systemsAnimation & motionAccessibility (a11y)CSS & TailwindUI designUXDesign to codeshadcn/uiLanding pagesBest design skills
Most searched skills
find-skillscavemanfrontend-designbrainstormingsystematic-debuggingstop-slopemil-design-engui-ux-pro-maxkarpathy-guidelinesweb-design-guidelinesreact-best-practicesgrill-meimprovemicrosoft-foundryagent-browser
Browse all listings
All agent skillsAll MCP serversAll marketplacesAll plugins

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.