implementing-taxii-server-with-opentaxii▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Deploy and configure an OpenTAXII server to share and consume STIX-formatted cyber threat intelligence using the TAXII 2.1 protocol for automated indicator exchange between organizations.
| name | implementing-taxii-server-with-opentaxii |
| description | Deploy and configure 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 | mahipal |
| 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
- 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,cabbylibraries - 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
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
Step 1: Deploy TAXII 2.1 Server with Medallion
# Install medallion (OASIS reference implementation)
# pip install medallion
# medallion_config.json
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
}
}
# Create initial data store
taxii_data = {
"discovery": {
"title": "Threat Intelligence TAXII Server",
"description": "TAXII 2.1 server for sharing CTI indicators",
"contact": "[email protected]",
"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
# docker-compose.yml
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", # Set SKIP_TLS_VERIFY=true for self-signed certs in lab environments
)
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
References
How to use implementing-taxii-server-with-opentaxii on Cursor
AI-first code editor with Composer
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 implementing-taxii-server-with-opentaxii
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches implementing-taxii-server-with-opentaxii from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate implementing-taxii-server-with-opentaxii. Access the skill through slash commands (e.g., /implementing-taxii-server-with-opentaxii) 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.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 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▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★70 reviews- ★★★★★Advait Martinez· Dec 28, 2024
implementing-taxii-server-with-opentaxii is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Carlos Jackson· Dec 24, 2024
implementing-taxii-server-with-opentaxii has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Sofia Shah· Dec 24, 2024
implementing-taxii-server-with-opentaxii fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Noah Ndlovu· Dec 20, 2024
Keeps context tight: implementing-taxii-server-with-opentaxii is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Noah Singh· Dec 16, 2024
implementing-taxii-server-with-opentaxii reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Noah Gonzalez· Dec 12, 2024
We added implementing-taxii-server-with-opentaxii from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ganesh Mohane· Dec 4, 2024
Keeps context tight: implementing-taxii-server-with-opentaxii is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Sakshi Patil· Nov 23, 2024
implementing-taxii-server-with-opentaxii has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Carlos Patel· Nov 19, 2024
implementing-taxii-server-with-opentaxii fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Advait Anderson· Nov 19, 2024
Solid pick for teams standardizing on skills: implementing-taxii-server-with-opentaxii is focused, and the summary matches what you get after install.
showing 1-10 of 70