Extract DPAPI-protected secrets such as credentials and browser data offline and online.
日本語の概要は準備中です。原文の説明を表示しています。
OpenCTI is an open-source platform for managing cyber threat intelligence knowledge, built on STIX 2.1 as its native data model. This skill covers building an automated IOC enrichment pipeline using O
インストールする前に、エージェントに与えられる指示の中身を確認できます。
OpenCTI is an open-source platform for managing cyber threat intelligence knowledge, built on STIX 2.1 as its native data model. This skill covers building an automated IOC enrichment pipeline using OpenCTI's connector ecosystem to enrich indicators with context from VirusTotal, Shodan, AbuseIPDB, GreyNoise, and other sources. The pipeline automatically enriches newly ingested indicators, correlates them with known threat actors and campaigns, and scores them for analyst prioritization.
pycti libraryOpenCTI uses a GraphQL API frontend backed by ElasticSearch for storage and Redis/RabbitMQ for connector communication. Data is natively stored as STIX 2.1 objects with relationships. Connectors are categorized as: External Import (feed ingestion), Internal Import (file parsing), Internal Enrichment (context addition), and Stream (real-time export).
Internal enrichment connectors are triggered automatically when new observables are created or manually by analysts. Each connector receives STIX objects, queries external services, and returns STIX 2.1 bundles that augment the original observable with additional context, labels, and relationships.
OpenCTI uses a 0-100 confidence scale for indicators. Enrichment connectors can update confidence scores based on external validation: VirusTotal detection ratios, Shodan exposure data, AbuseIPDB report counts, and GreyNoise classification results.
# docker-compose.yml (key services)
version: '3'
services:
opencti:
image: opencti/platform:6.4.4
environment:
- APP__PORT=8080
- APP__ADMIN__EMAIL=admin@opencti.io
- APP__ADMIN__PASSWORD=ChangeMeNow
- APP__ADMIN__TOKEN=your-admin-token-uuid
- ELASTICSEARCH__URL=http://elasticsearch:9200
- MINIO__ENDPOINT=minio
- RABBITMQ__HOSTNAME=rabbitmq
ports:
- "8080:8080"
depends_on:
- elasticsearch
- minio
- rabbitmq
- redis
connector-virustotal:
image: opencti/connector-virustotal:6.4.4
environment:
- OPENCTI_URL=http://opencti:8080
- OPENCTI_TOKEN=your-admin-token-uuid
- CONNECTOR_ID=connector-virustotal-id
- CONNECTOR_NAME=VirusTotal
- CONNECTOR_SCOPE=StixFile,Artifact,IPv4-Addr,Domain-Name,Url
- CONNECTOR_AUTO=true
- VIRUSTOTAL_TOKEN=your-vt-api-key
- VIRUSTOTAL_MAX_TLP=TLP:AMBER
connector-shodan:
image: opencti/connector-shodan:6.4.4
environment:
- OPENCTI_URL=http://opencti:8080
- OPENCTI_TOKEN=your-admin-token-uuid
- CONNECTOR_ID=connector-shodan-id
- CONNECTOR_NAME=Shodan
- CONNECTOR_SCOPE=IPv4-Addr
- CONNECTOR_AUTO=true
- SHODAN_TOKEN=your-shodan-api-key
- SHODAN_MAX_TLP=TLP:AMBER
connector-abuseipdb:
image: opencti/connector-abuseipdb:6.4.4
environment:
- OPENCTI_URL=http://opencti:8080
- OPENCTI_TOKEN=your-admin-token-uuid
- CONNECTOR_ID=connector-abuseipdb-id
- CONNECTOR_NAME=AbuseIPDB
- CONNECTOR_SCOPE=IPv4-Addr
- CONNECTOR_AUTO=true
- ABUSEIPDB_API_KEY=your-abuseipdb-key
import os
from pycti import OpenCTIConnectorHelper, get_config_variable
from stix2 import (
Bundle, Indicator, Note, Relationship,
IPv4Address, DomainName
)
import requests
class CustomEnrichmentConnector:
def __init__(self):
config = {
"opencti": {
"url": os.environ.get("OPENCTI_URL"),
"token": os.environ.get("OPENCTI_TOKEN"),
},
"connector": {
"id": os.environ.get("CONNECTOR_ID"),
"name": "CustomEnrichment",
"scope": "IPv4-Addr,Domain-Name,Url",
"auto": True,
"type": "INTERNAL_ENRICHMENT",
},
}
self.helper = OpenCTIConnectorHelper(config)
self.helper.listen(self._process_message)
def _process_message(self, data):
entity_id = data["entity_id"]
stix_object = self.helper.api.stix_cyber_observable.read(id=entity_id)
if not stix_object:
return "Observable not found"
observable_type = stix_object["entity_type"]
observable_value = stix_object.get("value", "")
enrichment_results = []
if observable_type == "IPv4-Addr":
enrichment_results = self._enrich_ip(observable_value, entity_id)
elif observable_type == "Domain-Name":
enrichment_results = self._enrich_domain(observable_value, entity_id)
if enrichment_results:
bundle = Bundle(objects=enrichment_results, allow_custom=True)
self.helper.send_stix2_bundle(bundle.serialize())
return "Enrichment completed"
def _enrich_ip(self, ip_address, entity_id):
"""Enrich IP address with GreyNoise, AbuseIPDB context."""
objects = []
# GreyNoise Community API
try:
gn_response = requests.get(
f"https://api.greynoise.io/v3/community/{ip_address}",
headers={"key": os.environ.get("GREYNOISE_API_KEY")},
timeout=30,
)
if gn_response.status_code == 200:
gn_data = gn_response.json()
classification = gn_data.get("classification", "unknown")
noise = gn_data.get("noise", False)
riot = gn_data.get("riot", False)
note_content = (
f"## GreyNoise Enrichment\n"
f"- Classification: {classification}\n"
f"- Internet Noise: {noise}\n"
f"- RIOT (Benign Service): {riot}\n"
f"- Name: {gn_data.get('name', 'N/A')}\n"
f"- Last Seen: {gn_data.get('last_seen', 'N/A')}"
)
note = Note(
content=note_content,
object_refs=[entity_id],
abstract=f"GreyNoise: {classification}",
allow_custom=True,
)
objects.append(note)
# Add labels based on classification
if classification == "malicious":
self.helper.api.stix_cyber_observable.add_label(
id=entity_id, label_name="greynoise:malicious"
)
elif riot:
self.helper.api.stix_cyber_observable.add_label(
id=entity_id, label_name="greynoise:benign-service"
)
except Exception as e:
self.helper.log_error(f"GreyNoise enrichment failed: {e}")
return objects
def _enrich_domain(self, domain, entity_id):
"""Enrich domain with WHOIS and DNS context."""
objects = []
try:
# Use SecurityTrails API for domain enrichment
st_response = requests.get(
f"https://api.securitytrails.com/v1/domain/{domain}",
headers={"APIKEY": os.environ.get("SECURITYTRAILS_API_KEY")},
timeout=30,
)
if st_response.status_code == 200:
st_data = st_response.json()
current_dns = st_data.get("current_dns", {})
a_records = [
r.get("ip") for r in current_dns.get("a", {}).get("values", [])
]
note_content = (
f"## SecurityTrails Enrichment\n"
f"- A Records: {', '.join(a_records)}\n"
f"- Alexa Rank: {st_data.get('alexa_rank', 'N/A')}\n"
f"- Hostname: {st_data.get('hostname', 'N/A')}"
)
note = Note(
content=note_content,
object_refs=[entity_id],
abstract=f"SecurityTrails: {domain}",
allow_custom=True,
)
objects.append(note)
except Exception as e:
self.helper.log_error(f"SecurityTrails enrichment failed: {e}")
return objects
if __name__ == "__main__":
connector = CustomEnrichmentConnector()
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Extract DPAPI-protected secrets such as credentials and browser data offline and online.
日本語の概要は準備中です。原文の説明を表示しています。
Audit, detect, and remediate Shadow Credentials misconfigurations on msDS-KeyCredentialLink in Active Directory. Focuses on access-controls, monitoring Event ID 5136, and credential-management hygiene.
日本語の概要は準備中です。原文の説明を表示しています。
Prepare a defense-contractor environment for CMMC Level 2 certification: scope CUI and FCI, implement the 110 NIST SP 800-171 Rev 2 security requirements across 14 families, compute the SPRS score with the DoD Assessment Methodology, manage a compliant POA&M, and ready the organization for a C3PAO assessment. Use when an organization handles Controlled Unclassified Information (CUI) under a DoD contract, when a contract carries DFARS clause 252.204-7012/7019/7020/7021, when preparing for or responding to a CMMC assessment, when computing or improving an SPRS score, when building a System Security Plan or POA&M for 800-171, or when scoping which systems are in the CUI boundary. Keywords: CMMC, CMMC Level 2, NIST 800-171, SP 800-171 Rev 2, CUI, FCI, SPRS, DFARS 7012, C3PAO, POA&M, System Security Plan, DoD Assessment Methodology, 110 controls, defense industrial base, DIB, FedRAMP equivalency.
日本語の概要は準備中です。原文の説明を表示しています。
Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
日本語の概要は準備中です。原文の説明を表示しています。
Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths
日本語の概要は準備中です。原文の説明を表示しています。
Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.
日本語の概要は準備中です。原文の説明を表示しています。