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implementing-taxii-server-with-opentaxii Use when deploying and configuring an OpenTAXII server to share and consume STIX-formatted cyber threat intelligence using the TAXII 2.1 protocol for automated indicator exchange between organizations.
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name implementing-taxii-server-with-opentaxii description Use when deploying and configuring an OpenTAXII server to share and consume STIX-formatted cyber threat intelligence using the TAXII 2.1 protocol for automated indicator exchange between organizations. domain cybersecurity subdomain threat-intelligence tags ["taxii","stix","opentaxii","threat-sharing","cti","indicator-exchange","taxii-server","automation"] version 1.0 author oyi77 license Apache-2.0 nist_csf ["ID.RA-01","ID.RA-05","DE.CM-01","DE.AE-02"]
Implementing TAXII Server with OpenTAXII
Overview
TAXII (Trusted Automated eXchange of Intelligence Information) is an OASIS standard protocol for exchanging cyber threat intelligence over HTTPS. OpenTAXII is an open-source TAXII server implementation by EclecticIQ that supports TAXII 1.x, while the OASIS cti-taxii-server provides a TAXII 2.1 reference implementation. This skill covers deploying a TAXII server, configuring collections for threat intelligence feeds, publishing STIX 2.1 bundles, and integrating with SIEM/SOAR platforms for automated indicator ingestion.
When to Use
Trigger phrases:
"implementing taxii server with opentaxii"
"Deploy and configure an OpenTAXII server to share and consume STIX-formatted cyb"
When deploying or configuring implementing taxii server with opentaxii capabilities in your environment
When establishing security controls aligned to compliance requirements
When building or improving security architecture for this domain
When conducting security assessments that require this implementation
Prerequisites
Python 3.9+ with medallion, stix2, taxii2-client, opentaxii, cabby libraries
Docker and Docker Compose for containerized deployment
Understanding of STIX 2.1 objects (Indicator, Malware, Attack Pattern, Relationship)
Familiarity with REST APIs and HTTPS configuration
TLS certificates for production deployment
Key Concepts
This section covers key concepts for implementing taxii server with opentaxii.
Ensure all prerequisites are met before proceeding
Follow the documented workflow steps in sequence
Record results and any anomalies encountered during this phase
TAXII 2.1 Architecture
TAXII 2.1 defines three services: Discovery (find available API roots), API Root (entry point for collections), and Collections (repositories of CTI objects). Collections support two access models: the Collection endpoint allows consumers to poll for objects, and the Status endpoint tracks the result of add operations. TAXII uses HTTP content negotiation with application/taxii+json;version=2.1.
Sharing Models
TAXII supports hub-and-spoke (central server distributes to consumers), peer-to-peer (bidirectional sharing between partners), and source-subscriber (producer publishes, consumers subscribe) models. Each collection can have read-only, write-only, or read-write access controls.
STIX 2.1 Content TAXII transports STIX 2.1 bundles containing Structured Threat Information objects: Indicators (detection patterns), Observed Data, Malware, Attack Patterns, Threat Actors, Intrusion Sets, Campaigns, Relationships, and Sightings. Each object has a unique STIX ID, creation/modification timestamps, and optional TLP marking definitions.
Workflow
Scope the task — define objectives, boundaries, and success criteriaGather information — collect all necessary data and context before proceedingExecute the core workflow — follow the domain-specific steps methodicallyValidate results — verify outputs against expected outcomes or baselinesDocument findings — record results, anomalies, and recommendations
Step 1: Deploy TAXII 2.1 Server with Medallion
import json
config = {
"backend" : {
"module_class" : "MemoryBackend" ,
"filename" : "taxii_data.json"
},
"users" : {
"admin" : "admin_password_change_me" ,
"analyst" : "analyst_password_change_me" ,
"readonly" : "readonly_password_change_me"
},
"taxii" : {
"max_content_length" : 10485760
}
}
taxii_data = {
"discovery" : {
"title" : "Threat Intelligence TAXII Server" ,
"description" : "TAXII 2.1 server for sharing CTI indicators" ,
"contact" : "soc@organization.com" ,
"default" : "https://taxii.organization.com/api/" ,
"api_roots" : ["https://taxii.organization.com/api/" ]
},
"api_roots" : {
"api" : {
"title" : "Threat Intelligence API Root" ,
"description" : "Primary API root for threat intelligence sharing" ,
"versions" : ["application/taxii+json;version=2.1" ],
"max_content_length" : 10485760 ,
"collections" : {
"malware-iocs" : {
"id" : "91a7b528-80eb-42ed-a74d-c6fbd5a26116" ,
"title" : "Malware IOCs" ,
"description" : "Indicators of compromise from malware analysis" ,
"can_read" : True ,
"can_write" : True ,
"media_types" : ["application/stix+json;version=2.1" ]
},
"apt-intelligence" : {
"id" : "52892447-4d7e-4f70-b94a-5460e242dd23" ,
"title" : "APT Intelligence" ,
"description" : "Advanced persistent threat group intelligence" ,
"can_read" : True ,
"can_write" : True ,
"media_types" : ["application/stix+json;version=2.1" ]
},
"phishing-indicators" : {
"id" : "64993447-4d7e-4f70-b94a-5460e242ee34" ,
"title" : "Phishing Indicators" ,
"description" : "Phishing URLs, domains, and email indicators" ,
"can_read" : True ,
"can_write" : True ,
"media_types" : ["application/stix+json;version=2.1" ]
}
}
}
}
}
with open ("medallion_config.json" , "w" ) as f:
json.dump(config, f, indent=2 )
with open ("taxii_data.json" , "w" ) as f:
json.dump(taxii_data, f, indent=2 )
print ("[+] TAXII server configuration created" )
Step 2: Docker Deployment
version: '3.8'
services:
taxii-server:
image: python:3.11-slim
container_name: taxii-server
working_dir: /app
volumes:
- ./medallion_config.json:/app/medallion_config.json
- ./taxii_data.json:/app/taxii_data.json
- ./certs:/app/certs
ports:
- "6100:6100"
command: >
bash -c "pip install medallion &&
medallion --host 0.0.0.0 --port 6100
--config /app/medallion_config.json"
restart: unless-stopped
healthcheck:
test: ["CMD" , "curl" , "-f" , "http://localhost:6100/taxii2/" ]
interval: 30s
timeout: 10s
retries: 3
Step 3: Publish STIX 2.1 Objects to Collections from stix2 import Indicator, Malware, Relationship, Bundle, TLP_WHITE
from taxii2client.v21 import Server, Collection, as_pages
import json
from datetime import datetime
class TAXIIPublisher :
def __init__ (self, server_url, username, password ):
self .server = Server(
server_url,
user=username,
password=password,
)
def list_collections (self ):
"""List all available collections."""
api_root = self .server.api_roots[0 ]
for collection in api_root.collections:
print (f" [{collection.id } ] {collection.title} "
f"(read={collection.can_read} , write={collection.can_write} )" )
return api_root.collections
def publish_indicators (self, collection_id, indicators ):
"""Publish STIX indicators to a TAXII collection."""
api_root = self .server.api_roots[0 ]
collection = Collection(
f"{api_root.url} collections/{collection_id} /" ,
user=self .server._user,
password=self .server._password,
)
bundle = Bundle(objects=indicators)
response = collection.add_objects(bundle.serialize())
print (f"[+] Published {len (indicators)} objects to {collection_id} " )
print (f" Status: {response.status} " )
return response
def create_malware_indicators (self ):
"""Create sample STIX malware indicators."""
malware = Malware(
name="SUNBURST" ,
description="Backdoor used in SolarWinds supply chain attack (2020). "
"Trojanized SolarWinds.Orion.Core.BusinessLayer.dll module." ,
malware_types=["backdoor" , "trojan" ],
is_family=True ,
object_marking_refs=[TLP_WHITE],
)
indicator_hash = Indicator(
name="SUNBURST SHA-256 Hash" ,
description="SHA-256 hash of trojanized SolarWinds Orion DLL" ,
pattern="[file:hashes.'SHA-256' = "
"'32519b85c0b422e4656de6e6c41878e95fd95026267daab4215ee59c107d6c77']" ,
pattern_type="stix" ,
valid_from=datetime(2020 , 12 , 13 ),
indicator_types=["malicious-activity" ],
object_marking_refs=[TLP_WHITE],
)
indicator_domain = Indicator(
name="SUNBURST C2 Domain Pattern" ,
description="DGA domain pattern used by SUNBURST for C2" ,
pattern="[domain-name:value MATCHES "
"'^[a-z0-9]{4,}\\.appsync-api\\..*\\.avsvmcloud\\.com$']" ,
pattern_type="stix" ,
valid_from=datetime(2020 , 12 , 13 ),
indicator_types=["malicious-activity" ],
object_marking_refs=[TLP_WHITE],
)
rel = Relationship(
relationship_type="indicates" ,
source_ref=indicator_hash.id ,
target_ref=malware.id ,
)
return [malware, indicator_hash, indicator_domain, rel]
publisher = TAXIIPublisher(
"https://taxii.organization.com/taxii2/" ,
"admin" , "admin_password_change_me"
)
collections = publisher.list_collections()
indicators = publisher.create_malware_indicators()
publisher.publish_indicators("91a7b528-80eb-42ed-a74d-c6fbd5a26116" , indicators)
Step 4: Consume Intelligence from TAXII Collections from taxii2client.v21 import Server, Collection, as_pages
import json
class TAXIIConsumer :
def __init__ (self, server_url, username, password ):
self .server = Server(server_url, user=username, password=password)
def poll_collection (self, collection_id, added_after=None ):
"""Poll a collection for new STIX objects."""
api_root = self .server.api_roots[0 ]
collection = Collection(
f"{api_root.url} collections/{collection_id} /" ,
user=self .server._user,
password=self .server._password,
)
kwargs = {}
if added_after:
kwargs["added_after" ] = added_after
all_objects = []
for bundle in as_pages(collection.get_objects, per_request=50 , **kwargs):
objects = json.loads(bundle).get("objects" , [])
all_objects.extend(objects)
indicators = [o for o in all_objects if o.get("type" ) == "indicator" ]
malware = [o for o in all_objects if o.get("type" ) == "malware" ]
relationships = [o for o in all_objects if o.get("type" ) == "relationship" ]
print (f"[+] Polled {len (all_objects)} objects: "
f"{len (indicators)} indicators, {len (malware)} malware, "
f"{len (relationships)} relationships" )
return all_objects
def extract_iocs_for_siem (self, stix_objects ):
"""Extract IOCs from STIX objects for SIEM ingestion."""
iocs = []
for obj in stix_objects:
if obj.get("type" ) == "indicator" :
pattern = obj.get("pattern" , "" )
iocs.append({
"id" : obj.get("id" ),
"name" : obj.get("name" , "" ),
"pattern" : pattern,
"valid_from" : obj.get("valid_from" , "" ),
"indicator_types" : obj.get("indicator_types" , []),
"confidence" : obj.get("confidence" , 0 ),
})
return iocs
consumer = TAXIIConsumer(
"https://taxii.organization.com/taxii2/" ,
"analyst" , "analyst_password_change_me"
)
objects = consumer.poll_collection("91a7b528-80eb-42ed-a74d-c6fbd5a26116" )
iocs = consumer.extract_iocs_for_siem(objects)
Step 5: Integrate with SIEM/SOAR import requests
def push_to_splunk (iocs, splunk_url, hec_token ):
"""Push extracted IOCs to Splunk via HEC."""
headers = {"Authorization" : f"Splunk {hec_token} " }
for ioc in iocs:
event = {
"event" : ioc,
"sourcetype" : "stix:indicator" ,
"source" : "taxii-server" ,
"index" : "threat_intel" ,
}
resp = requests.post(
f"{splunk_url} /services/collector/event" ,
headers=headers,
json=event,
verify=not os.environ.get("SKIP_TLS_VERIFY" , "" ).lower() == "true" ,
)
if resp.status_code != 200 :
print (f"[-] Splunk HEC error: {resp.text} " )
print (f"[+] Pushed {len (iocs)} IOCs to Splunk" )
def push_to_elasticsearch (iocs, es_url, index="threat-intel" ):
"""Push IOCs to Elasticsearch."""
for ioc in iocs:
resp = requests.post(
f"{es_url} /{index} /_doc" ,
json=ioc,
headers={"Content-Type" : "application/json" },
)
if resp.status_code not in (200 , 201 ):
print (f"[-] ES error: {resp.text} " )
print (f"[+] Indexed {len (iocs)} IOCs in Elasticsearch" )
Validation Criteria
TAXII 2.1 server deployed and accessible via HTTPS
Collections created with appropriate read/write permissions
STIX 2.1 bundles published successfully to collections
Consumer can poll and retrieve objects with filtering
IOCs extracted and forwarded to SIEM platform
Authentication and authorization enforced correctly
When NOT to Use
You need to test the implementation (use performing-* skills)
Task is about configuring existing tools (use configuring-* skills)
You need to analyze security events (use analyzing-* skills)
Task is about building detection rules (use building-* skills)
You don't have access to the target environment
Task requires vendor-specific expertise (consult vendor docs)
Red Flags
Performing actions without explicit written authorization from the asset owner
Testing against production systems without a defined scope and rules of engagement
Acting on threat intelligence without validating source reliability
Sharing classified or sensitive indicators without proper handling procedures
Alerting threat actors to detection capabilities through visible response actions
Verification
All steps executed successfully against a test environment before production use
Output documented with screenshots or logs demonstrating expected behavior
Results validated against known-good baselines or reference implementations
Documentation complete enough for another analyst to reproduce findings
References
Process
Analyze the task requirements
Apply domain expertise
Verify output quality
Anti-Rationalization Table Rationalization Reality "We are too small to be targeted" Automated attacks target everyone. Size does not matter. "Security slows us down" A breach slows you down 100x more. Build security in from the start. "We will fix it after launch" Vulnerabilities in production are exploited within hours. Fix before deploy.
