Implementing Security Information Sharing With Stix2

Implementing Security Information Sharing with STIX 2.1

Build and share structured threat intelligence using STIX 2.1 objects with the stix2 Python library and TAXII 2.1 transport protocol.

When to Use

• Building a threat intelligence platform that exchanges IOCs with partner organizations
• Automating ingestion and export of indicators from MISP, OpenCTI, or other TIP platforms
• Creating machine-readable intelligence reports for ISAC/ISAO sharing communities
• Publishing threat data to a TAXII 2.1 server for downstream consumption by SIEMs and SOARs
• Converting unstructured threat reports into standardized STIX 2.1 bundles
• Enriching detection rules with context by linking indicators to malware, campaigns, and threat actors

Do not use for sharing simple IP blocklists or CSV-based IOC feeds that do not require relationship context; plain-text feeds with simpler formats like CSV or OpenIOC may be more efficient in those cases.

Prerequisites

• Python 3.8+ with stix2 library (pip install stix2)
• taxii2-client for consuming TAXII feeds (pip install taxii2-client)
• A TAXII 2.1 server endpoint for publishing (e.g., OpenTAXII, Medallion, or MISP TAXII service)
• Familiarity with STIX 2.1 SDO types: Indicator, Malware, Threat Actor, Campaign, Attack Pattern, Identity
• Familiarity with STIX 2.1 SRO types: Relationship, Sighting
• Optional: OpenCTI or MISP instance for end-to-end integration testing

Workflow

Step 1: Install Dependencies

pip install stix2 taxii2-client requests

Step 2: Create STIX 2.1 Domain Objects (SDOs)

Create core intelligence objects that describe threats, actors, and campaigns:

from stix2 import (
    Indicator, Malware, ThreatActor, Campaign,
    AttackPattern, Identity, Relationship, Bundle,
    ExternalReference
)
from datetime import datetime

# Create a producer identity
producer = Identity(
    name="ACME Threat Intel Team",
    identity_class="organization",
    sectors=["technology"],
    contact_information="threatintel@acme.example.com"
)

# Create a malware object
emotet_malware = Malware(
    name="Emotet",
    description="Banking trojan turned modular botnet loader. "
                "Distributed via malspam with macro-enabled Office documents.",
    malware_types=["trojan", "bot"],
    is_family=True,
    created_by_ref=producer.id
)

# Create an attack pattern referencing MITRE ATT&CK
spearphishing_pattern = AttackPattern(
    name="Spearphishing Attachment",
    description="Adversaries send spearphishing emails with a malicious attachment.",
    external_references=[
        ExternalReference(
            source_name="mitre-attack",
            external_id="T1566.001",
            url="https://attack.mitre.org/techniques/T1566/001/"
        )
    ],
    created_by_ref=producer.id
)

# Create a threat actor
threat_actor = ThreatActor(
    name="Mummy Spider",
    description="Cybercriminal group operating the Emotet botnet infrastructure.",
    threat_actor_types=["crime-syndicate"],
    aliases=["TA542", "Gold Crestwood"],
    primary_motivation="personal-gain",
    created_by_ref=producer.id
)

# Create a campaign
campaign = Campaign(
    name="Emotet Q1 2026 Resurgence",
    description="Renewed Emotet distribution campaign using thread-hijacked "
                "reply-chain emails with OneNote lure attachments.",
    first_seen="2026-01-15T00:00:00Z",
    created_by_ref=producer.id
)

print(f"Created malware SDO: {emotet_malware.id}")
print(f"Created threat actor SDO: {threat_actor.id}")
print(f"Created campaign SDO: {campaign.id}")

Step 3: Create STIX Indicators with Patterns

Define detection patterns using the STIX Patterning Language:

# File hash indicator
hash_indicator = Indicator(
    name="Emotet dropper hash",
    description="SHA-256 hash of Emotet first-stage dropper observed in Jan 2026 campaign.",
    indicator_types=["malicious-activity"],
    pattern_type="stix",
    pattern="[file:hashes.'SHA-256' = 'a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5f6a1b2']",
    valid_from="2026-01-15T00:00:00Z",
    created_by_ref=producer.id
)

# Network indicator for C2 domain
c2_indicator = Indicator(
    name="Emotet C2 domain",
    description="Command and control domain observed in Emotet tier-1 botnet infrastructure.",
    indicator_types=["malicious-activity"],
    pattern_type="stix",
    pattern="[domain-name:value = 'malicious-c2.example.com']",
    valid_from="2026-01-20T00:00:00Z",
    created_by_ref=producer.id
)

# Compound pattern: process spawning with suspicious command line
process_indicator = Indicator(
    name="Emotet PowerShell download cradle",
    description="PowerShell execution pattern used by Emotet to download next-stage payload.",
    indicator_types=["malicious-activity"],
    pattern_type="stix",
    pattern=(
        "[process:command_line MATCHES 'powershell.*-enc.*' "
        "AND process:parent_ref.name = 'winword.exe']"
    ),
    valid_from="2026-01-15T00:00:00Z",
    created_by_ref=producer.id
)

# Email subject indicator
email_indicator = Indicator(
    name="Emotet phishing subject line pattern",
    description="Subject line pattern seen in thread-hijacked Emotet phishing emails.",
    indicator_types=["malicious-activity"],
    pattern_type="stix",
    pattern="[email-message:subject MATCHES '^RE:.*Invoice.*[0-9]{6}']",
    valid_from="2026-01-15T00:00:00Z",
    created_by_ref=producer.id
)

print(f"Created {4} indicator objects")

Step 4: Build Relationships Between Objects

Link SDOs together using Relationship objects to express how threats are connected:

# Malware uses attack pattern
rel_malware_attack = Relationship(
    relationship_type="uses",
    source_ref=emotet_malware.id,
    target_ref=spearphishing_pattern.id,
    description="Emotet is distributed via spearphishing attachments.",
    created_by_ref=producer.id
)

# Threat actor uses malware
rel_actor_malware = Relationship(
    relationship_type="uses",
    source_ref=threat_actor.id,
    target_ref=emotet_malware.id,
    description="Mummy Spider operates the Emotet malware infrastructure.",
    created_by_ref=producer.id
)

# Indicator indicates malware
rel_indicator_malware = Relationship(
    relationship_type="indicates",
    source_ref=hash_indicator.id,
    target_ref=emotet_malware.id,
    description="File hash indicator for Emotet dropper binary.",
    created_by_ref=producer.id
)

# Campaign uses malware
rel_campaign_malware = Relationship(
    relationship_type="uses",
    source_ref=campaign.id,
    target_ref=emotet_malware.id,
    created_by_ref=producer.id
)

# Threat actor attributed to campaign
rel_actor_campaign = Relationship(
    relationship_type="attributed-to",
    source_ref=campaign.id,
    target_ref=threat_actor.id,
    created_by_ref=producer.id
)

print(f"Created {5} relationship objects linking threat intelligence")

Step 5: Assemble and Serialize a STIX Bundle

Package all objects into a bundle for sharing:

import json

bundle = Bundle(
    objects=[
        producer,
        emotet_malware,
        spearphishing_pattern,
        threat_actor,
        campaign,
        hash_indicator,
        c2_indicator,
        process_indicator,
        email_indicator,
        rel_malware_attack,
        rel_actor_malware,
        rel_indicator_malware,
        rel_campaign_malware,
        rel_actor_campaign,
    ]
)

# Serialize to JSON
bundle_json = bundle.serialize(pretty=True)

# Write bundle to file for sharing
with open("emotet_campaign_bundle.json", "w") as f:
    f.write(bundle_json)

print(f"Bundle {bundle.id} contains {len(bundle.objects)} objects")
print(f"Written to emotet_campaign_bundle.json")

# Validate the bundle by re-parsing
from stix2 import parse
parsed = parse(bundle_json, allow_custom=False)
print(f"Bundle validation passed: {len(parsed.objects)} objects parsed successfully")

Step 6: Consume Intelligence from a TAXII 2.1 Server

Retrieve published threat intelligence from a TAXII feed:

from taxii2client.v21 import Server, Collection, as_pages
import json

# Connect to a TAXII 2.1 server
taxii_server = Server(
    "https://taxii.example.com/taxii2/",
    user="readonly",
    password="readonly_password"
)

# Discover API roots and collections
api_root = taxii_server.api_roots[0]
print(f"API Root: {api_root.title}")

for collection in api_root.collections:
    print(f"  Collection: {collection.title} (ID: {collection.id})")

# Fetch indicators from a specific collection
target_collection = Collection(
    f"https://taxii.example.com/taxii2/collections/{api_root.collections[0].id}/",
    user="readonly",
    password="readonly_password"
)

# Retrieve objects with filtering
response = target_collection.get_objects(
    added_after="2026-01-01T00:00:00Z",
    type=["indicator", "malware"]
)

stix_data = json.loads(response.text)
print(f"Retrieved {len(stix_data.get('objects', []))} objects from TAXII server")

# Process each retrieved object
for obj in stix_data.get("objects", []):
    if obj["type"] == "indicator":
        print(f"  Indicator: {obj['name']} | Pattern: {obj['pattern'][:60]}...")
    elif obj["type"] == "malware":
        print(f"  Malware: {obj['name']} | Family: {obj.get('is_family', False)}")

Step 7: Publish Intelligence to a TAXII 2.1 Server

Push your STIX bundle to a writable TAXII collection:

import requests
import json

TAXII_URL = "https://taxii.example.com/taxii2/collections/COLLECTION_ID/objects/"
TAXII_USER = "publisher"
TAXII_PASS = "publisher_password"

headers = {
    "Content-Type": "application/taxii+json;version=2.1",
    "Accept": "application/taxii+json;version=2.1"
}

# Read the bundle we created earlier
with open("emotet_campaign_bundle.json", "r") as f:
    bundle_data = f.read()

response = requests.post(
    TAXII_URL,
    headers=headers,
    auth=(TAXII_USER, TAXII_PASS),
    data=bundle_data,
    timeout=30
)

if response.status_code in (200, 201, 202):
    status = response.json()
    print(f"Published successfully. Status ID: {status.get('id')}")
    print(f"  Total count: {status.get('total_count')}")
    print(f"  Success count: {status.get('success_count')}")
    print(f"  Failure count: {status.get('failure_count')}")
else:
    print(f"Publishing failed: {response.status_code} - {response.text}")

Step 8: Validate and Lint STIX Objects

Ensure objects comply with the STIX 2.1 specification:

from stix2 import parse, exceptions
import json

def validate_stix_bundle(bundle_path):
    """Validate all objects in a STIX bundle against the 2.1 spec."""
    with open(bundle_path, "r") as f:
        raw = json.load(f)

    errors = []
    valid_count = 0

    for obj in raw.get("objects", []):
        try:
            parsed = parse(json.dumps(obj), allow_custom=False)
            valid_count += 1
        except (exceptions.InvalidValueError, exceptions.MissingPropertiesError) as e:
            errors.append({
                "object_id": obj.get("id", "unknown"),
                "object_type": obj.get("type", "unknown"),
                "error": str(e)
            })

    print(f"Validation results: {valid_count} valid, {len(errors)} errors")
    for err in errors:
        print(f"  ERROR in {err['object_type']} ({err['object_id']}): {err['error']}")

    return len(errors) == 0

validate_stix_bundle("emotet_campaign_bundle.json")

Verification

• Confirm all STIX objects serialize to valid JSON and include required properties (type, id, created, modified)
• Verify relationship source_ref and target_ref point to existing object IDs within the bundle
• Validate indicator patterns parse correctly using the STIX patterning grammar
• Test TAXII publishing returns a success status with success_count matching the number of objects sent
• Re-retrieve published objects from the TAXII server and confirm they round-trip without data loss
• Check that consuming systems (SIEM, SOAR, TIP) can ingest the bundle and create corresponding detection rules or enrichment data
• Run stix2-validator CLI tool against exported bundles: stix2_validator emotet_campaign_bundle.json