What it does. Extracts indicators of compromise (IOCs) from malware samples, including Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).

Install

• npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-iocs-from-malware-samples, or copy the skill folder into ~/.claude/skills/extracting-iocs-from-malware-samples/.
• Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/extracting-iocs-from-malware-samples/SKILL.md

SKILL.md (verbatim)

1 placeholder credential was shortened (for example to api_key=YOUR_KEY) to pass the site's secret filter.

name: extracting-iocs-from-malware-samples
description: Extracts indicators of compromise (IOCs) from malware samples, including
  file hashes, network indicators (IPs, domains, URLs, PCAP indicators), host artifacts
  (file paths, registry keys, mutexes), and behavioral patterns, using tools like
  CyberChef, then defangs and exports them in standard threat-intel formats. Use
  for IOC extraction, threat indicator harvesting, or building detection content
  from a sample.
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware
- IOC-extraction
- threat-intelligence
- indicators
- detection
version: 1.0.0
author: mahipal
license: Apache-2.0
nist_csf:
- DE.AE-02
- RS.AN-03
- ID.RA-01
- DE.CM-01
mitre_attack:
- T1027
- T1055
- T1140
- T1497

Extracting IOCs from Malware Samples

When to Use

• A malware analysis (static or dynamic) is complete and actionable indicators need to be extracted for defense teams
• Building blocklists for firewalls, proxies, and DNS sinkholes from analyzed samples
• Creating YARA rules, Snort/Suricata signatures, or SIEM detection content from malware artifacts
• Contributing to threat intelligence sharing platforms (MISP, OTX, ThreatConnect)
• Tracking malware campaigns by correlating IOCs across multiple samples

Do not use for IOCs from unverified sources without validation; false positives in blocklists can disrupt legitimate business operations.

Prerequisites

• Python 3.8+ with iocextract, pefile, yara-python libraries installed
• Completed malware analysis report (static analysis, dynamic analysis, or reverse engineering)
• Access to PCAP files, memory dumps, or sandbox reports from the analysis
• MISP instance or STIX/TAXII server for structured IOC sharing
• VirusTotal API key for IOC enrichment and validation
• CyberChef for decoding obfuscated indicators

Workflow

Step 1: Extract File-Based IOCs

Compute hashes and identify file metadata indicators:

# Generate all standard hashes
md5sum malware_sample.exe
sha1sum malware_sample.exe
sha256sum malware_sample.exe

# Generate ssdeep fuzzy hash for similarity matching
ssdeep malware_sample.exe

# Generate imphash (import hash) for PE files
python3 -c "
import pefile
pe = pefile.PE('malware_sample.exe')
print(f'Imphash: {pe.get_imphash()}')
"

# Generate TLSH (Trend Micro Locality Sensitive Hash)
python3 -c "
import tlsh
with open('malware_sample.exe', 'rb') as f:
    h = tlsh.hash(f.read())
print(f'TLSH: {h}')
"

# Compile file metadata IOCs
python3 << 'PYEOF'
import pefile
import os
import hashlib
import datetime

pe = pefile.PE("malware_sample.exe")

print("FILE IOCs:")
with open("malware_sample.exe", "rb") as f:
    data = f.read()
    print(f"  MD5:        {hashlib.md5(data).hexdigest()}")
    print(f"  SHA-1:      {hashlib.sha1(data).hexdigest()}")
    print(f"  SHA-256:    {hashlib.sha256(data).hexdigest()}")
    print(f"  File Size:  {len(data)} bytes")

ts = pe.FILE_HEADER.TimeDateStamp
print(f"  Compile:    {datetime.datetime.utcfromtimestamp(ts)} UTC")
print(f"  Imphash:    {pe.get_imphash()}")
PYEOF

Step 2: Extract Network IOCs

Pull network indicators from strings, PCAP, and sandbox reports:

# Extract network IOCs from strings
import re

with open("malware_sample.exe", "rb") as f:
    data = f.read()

# Extract ASCII and Unicode strings
ascii_strings = re.findall(b'[ -~]{4,}', data)
unicode_strings = re.findall(b'(?:[ -~]\x00){4,}', data)

all_strings = [s.decode('ascii', errors='ignore') for s in ascii_strings]
all_strings += [s.decode('utf-16-le', errors='ignore') for s in unicode_strings]

# IP addresses (excluding private ranges for C2 indicators)
ip_pattern = re.compile(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')
ips = set()
for s in all_strings:
    for ip in ip_pattern.findall(s):
        # Filter out private/reserved ranges
        octets = [int(o) for o in ip.split('.')]
        if octets[0] not in [10, 127, 0] and not (octets[0] == 172 and 16 <= octets[1] <= 31) and not (octets[0] == 192 and octets[1] == 168):
            ips.add(ip)

# Domain names
domain_pattern = re.compile(r'\b[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z]{2,})+\b')
domains = set()
for s in all_strings:
    for d in domain_pattern.findall(s):
        if not d.endswith(('.dll', '.exe', '.sys', '.com.au')):
            domains.add(d)

# URLs
url_pattern = re.compile(r'https?://[^\s<>"{}|\\^`\[\]]+')
urls = set()
for s in all_strings:
    for u in url_pattern.findall(s):
        urls.add(u)

print("NETWORK IOCs:")
print(f"  IPs:     {ips}")
print(f"  Domains: {domains}")
print(f"  URLs:    {urls}")

Step 3: Extract Host-Based IOCs

Identify file paths, registry keys, mutexes, and services:

# Extract host-based IOCs from sandbox report
import json

with open("cuckoo_report.json") as f:
    report = json.load(f)

print("HOST IOCs:")

# File paths created or modified
print("\nFile Paths:")
for f in report["behavior"]["summary"].get("files", []):
    if any(p in f.lower() for p in ["temp", "appdata", "system32", "programdata"]):
        print(f"  [DROPPED] {f}")

# Registry keys for persistence
print("\nRegistry Keys:")
for key in report["behavior"]["summary"].get("write_keys", []):
    if any(p in key.lower() for p in ["run", "service", "startup", "shell"]):
        print(f"  [PERSIST] {key}")

# Mutexes (unique to malware family)
print("\nMutexes:")
for mutex in report["behavior"]["summary"].get("mutexes", []):
    if mutex not in ["Local\\!IETld!Mutex", "RasPbFile"]:  # Filter known Windows mutexes
        print(f"  [MUTEX] {mutex}")

# Created services
print("\nServices:")
for svc in report["behavior"]["summary"].get("started_services", []):
    print(f"  [SERVICE] {svc}")

Step 4: Extract Network IOCs from PCAP

Parse network captures for additional indicators:

# Extract DNS queries from PCAP
tshark -r capture.pcap -T fields -e dns.qry.name -Y "dns.flags.response == 0" | sort -u

# Extract HTTP hosts and URLs
tshark -r capture.pcap -T fields -e http.host -e http.request.uri -Y "http.request" | sort -u

# Extract TLS server names (SNI)
tshark -r capture.pcap -T fields -e tls.handshake.extensions_server_name -Y "tls.handshake.type == 1" | sort -u

# Extract JA3 hashes
tshark -r capture.pcap -T fields -e tls.handshake.ja3 -Y "tls.handshake.type == 1" | sort -u

# Extract unique destination IPs
tshark -r capture.pcap -T fields -e ip.dst -Y "ip.src == 10.0.2.15" | sort -u

# Extract User-Agent strings
tshark -r capture.pcap -T fields -e http.user_agent -Y "http.user_agent" | sort -u

Step 5: Defang and Validate IOCs

Defang indicators for safe sharing and validate against threat intelligence:

# Defang IOCs for safe sharing
def defang_ip(ip):
    return ip.replace(".", "[.]")

def defang_url(url):
    return url.replace("http", "hxxp").replace(".", "[.]")

def defang_domain(domain):
    return domain.replace(".", "[.]")

# Validate IOCs against VirusTotal
import requests

VT_API_KEY = YOUR_KEY

def check_vt_ip(ip):
    resp = requests.get(f"https://www.virustotal.com/api/v3/ip_addresses/{ip}",
                       headers={"x-apikey": VT_API_KEY})
    data = resp.json()
    stats = data["data"]["attributes"]["last_analysis_stats"]
    return stats["malicious"]

def check_vt_domain(domain):
    resp = requests.get(f"https://www.virustotal.com/api/v3/domains/{domain}",
                       headers={"x-apikey": VT_API_KEY})
    data = resp.json()
    stats = data["data"]["attributes"]["last_analysis_stats"]
    return stats["malicious"]

# Validate each IOC
for ip in ips:
    detections = check_vt_ip(ip)
    print(f"  {defang_ip(ip)} - VT: {detections} detections")

Step 6: Export IOCs in Standard Formats

Generate structured IOC outputs for sharing and ingestion:

# Export as STIX 2.1 bundle
from stix2 import Indicator, Bundle, Malware, Relationship
import datetime

indicators = []

# File hash indicator
indicators.append(Indicator(
    name="Malware SHA-256 Hash",
    pattern=f"[file:hashes.'SHA-256' = '{sha256_hash}']",
    pattern_type="stix",
    valid_from=datetime.datetime.now(datetime.timezone.utc),
    labels=["malicious-activity"]
))

# IP indicator
for ip in ips:
    indicators.append(Indicator(
        name=f"C2 IP Address {ip}",
        pattern=f"[ipv4-addr:value = '{ip}']",
        pattern_type="stix",
        valid_from=datetime.datetime.now(datetime.timezone.utc),
        labels=["malicious-activity"]
    ))

# Domain indicator
for domain in domains:
    indicators.append(Indicator(
        name=f"C2 Domain {domain}",
        pattern=f"[domain-name:value = '{domain}']",
        pattern_type="stix",
        valid_from=datetime.datetime.now(datetime.timezone.utc),
        labels=["malicious-activity"]
    ))

bundle = Bundle(objects=indicators)
with open("iocs_stix.json", "w") as f:
    f.write(bundle.serialize(pretty=True))

# Export as CSV for SIEM ingestion
import csv
with open("iocs.csv", "w", newline="") as f:
    writer = csv.writer(f)
    writer.writerow(["type", "value", "context", "confidence"])
    writer.writerow(["sha256", sha256_hash, "malware_sample", "high"])
    for ip in ips:
        writer.writerow(["ipv4", ip, "c2_server", "high"])
    for domain in domains:
        writer.writerow(["domain", domain, "c2_domain", "high"])
    for url in urls:
        writer.writerow(["url", url, "c2_url", "high"])

Key Concepts

Tools & Systems

• iocextract (Python): Automated IOC extraction library supporting IPs, URLs, domains, hashes, and YARA rules from text
• MISP: Open-source threat intelligence sharing platform for structured IOC management and distribution
• CyberChef: Web-based tool for decoding, decrypting, and transforming data useful for deobfuscating encoded IOCs
• tshark: Command-line network protocol analyzer for extracting network IOCs from PCAP files
• VirusTotal: Online service for validating and enriching IOCs with community detection results and threat intelligence

Common Scenarios

Scenario: Building a Comprehensive IOC Package from a Ransomware Sample

Context: A ransomware incident requires rapid IOC extraction for blocking across the enterprise while the full investigation continues. Multiple data sources are available: the sample binary, PCAP from network monitoring, and a Cuckoo sandbox report.

Approach:

1. Compute all file hashes (MD5, SHA-1, SHA-256, imphash, ssdeep) for the ransomware binary and any dropped files
2. Extract network IOCs from strings in the binary (hardcoded C2 addresses)
3. Parse the PCAP for DNS queries, HTTP requests, and TLS SNI fields
4. Extract host IOCs from the sandbox report (file paths, registry keys, mutexes, ransom note filenames)
5. Validate all network IOCs against VirusTotal to confirm malicious status and check for known associations
6. Defang all indicators and compile into STIX 2.1 format for sharing and CSV for SIEM ingestion
7. Submit to MISP event for organizational and community sharing

Pitfalls:

• Including IP addresses of legitimate CDNs or cloud services without validating context (e.g., AWS IPs used for hosting, not inherently malicious)
• Not defanging URLs and IPs in reports, leading to accidental clicks or DNS resolution
• Extracting strings from packed binaries (IOCs from packed samples are unreliable; unpack first)
• Forgetting to include dropped file hashes (the initial dropper and the final payload are separate IOCs)

Output Format

IOC EXTRACTION REPORT
======================
Sample:           ransomware.exe
Analysis Date:    2025-09-15
Analyst:          [Name]

FILE INDICATORS
SHA-256:          e3b0c44298fc1c149afbf4c8996fb924...
SHA-1:            da39a3ee5e6b4b0d3255bfef95601890afd80709
MD5:              d41d8cd98f00b204e9800998ecf8427e
Imphash:          a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6
ssdeep:           3072:kJh3bN7fY+aUkJh3bN7fY+aU:kJh3R7aUkJh3R7aU

NETWORK INDICATORS
C2 IPs:           185.220.101[.]42, 91.215.85[.]17
C2 Domains:       update.malicious[.]com, backup.evil[.]net
C2 URLs:          hxxps://update.malicious[.]com/gate.php
                  hxxps://backup.evil[.]net/gate.php
JA3 Hash:         a0e9f5d64349fb13191bc781f81f42e1
User-Agent:       Mozilla/5.0 (compatible; MSIE 10.0)

HOST INDICATORS
File Paths:       C:\Users\Public\svchost.exe
                  C:\Users\%USER%\AppData\Local\Temp\payload.dll
                  C:\Users\%USER%\Desktop\README_DECRYPT.txt
Registry Keys:    HKCU\Software\Microsoft\Windows\CurrentVersion\Run\WindowsUpdate
Mutexes:          Global\CryptLocker_2025_Q3
Services:         FakeWindowsUpdate

CONFIDENCE ASSESSMENT
High Confidence:  SHA-256, C2 IPs (validated via VT), Mutexes
Medium Confidence: Domains (could be compromised legitimate sites)
Low Confidence:   User-Agent (common string, high false positive risk)

EXPORT FILES
stix_bundle.json  - STIX 2.1 format for TIP ingestion
iocs.csv          - Flat CSV for SIEM blocklist import
yara_rule.yar     - YARA detection rule

Other files in this skill

• LICENSE
• references/api-reference.md
• scripts/agent.py

references/api-reference.md (verbatim)

API Reference: Malware IOC Extraction Agent

Dependencies

CLI Usage

python scripts/agent.py \
  --sample /cases/malware.exe \
  --yara-rules /rules/malware.yar \
  --vt-key YOUR_VT_API_KEY \
  --output-dir /cases/analysis/ \
  --output ioc_report.json

Functions

compute_hashes(file_path) -> dict

Computes MD5, SHA-1, SHA-256 and file size for the sample.

extract_pe_metadata(file_path) -> dict

Parses PE headers via pefile: imphash, compile timestamp, section entropy, import table.

extract_strings(file_path, min_length) -> list

Extracts ASCII and Unicode strings (min 4 chars) from the binary.

extract_network_iocs(strings) -> dict

Regex extraction of IPs, domains, URLs, emails from strings. Filters private IP ranges.

extract_host_iocs(strings) -> dict

Identifies Windows file paths, registry keys, and mutex names from strings.

run_yara_scan(file_path, rules_path) -> list

Compiles and runs YARA rules against the sample. Returns matched rule names, tags, and string offsets.

validate_ioc_virustotal(ioc_value, ioc_type, api_key) -> dict

Queries VirusTotal API v3 for IP, domain, or file hash. Returns malicious/suspicious counts.

defang_ioc(value) -> str

Defangs IOCs by replacing http with hxxp and . with [.].

export_stix_bundle(iocs, sha256) -> dict

Builds a STIX 2.1 indicator bundle with file hash, IP, and domain patterns.

export_csv(iocs, hashes, output_path)

Writes IOCs to CSV format (type, value, context, confidence) for SIEM ingestion.

run_extraction(sample_path, output_dir, yara_rules, vt_key) -> dict

Orchestrates the full extraction pipeline and generates all output files.

Regex Patterns

Output Schema

{
  "hashes": {"md5": "...", "sha256": "...", "sha1": "..."},
  "pe_metadata": {"imphash": "...", "compile_time": "...", "sections": []},
  "network_iocs": {"ips": [], "domains": [], "urls": []},
  "host_iocs": {"file_paths": [], "registry_keys": [], "mutexes": []},
  "yara_matches": [{"rule": "APT28_dropper", "tags": ["apt"]}],
  "summary": {"ips": 3, "domains": 5, "yara_hits": 1}
}

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