external/anthropic-cybersecurity-skills/skills/analyzing-indicators-of-compromise/SKILL.md
Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with multi-source intelligence; or making block/monitor/whitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.
npx skillsauth add seikaikyo/dash-skills analyzing-indicators-of-compromiseInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Use this skill when:
Do not use this skill in isolation for high-stakes blocking decisions — always combine automated enrichment with analyst judgment, especially for shared infrastructure (CDNs, cloud providers).
requests and vt-py libraries, or SOAR platform with pre-built connectorsBefore enriching, classify each IOC:
evil[.]com), extract registered domain via tldextractDefang IOCs in documentation (replace . with [.] and :// with [://]) to prevent accidental clicks.
VirusTotal (file hash, URL, IP, domain):
import vt
client = vt.Client("YOUR_VT_API_KEY")
# File hash lookup
file_obj = client.get_object(f"/files/{sha256_hash}")
detections = file_obj.last_analysis_stats
print(f"Malicious: {detections['malicious']}/{sum(detections.values())}")
# Domain analysis
domain_obj = client.get_object(f"/domains/{domain}")
print(domain_obj.last_analysis_stats)
print(domain_obj.reputation)
client.close()
AbuseIPDB (IP addresses):
import requests
response = requests.get(
"https://api.abuseipdb.com/api/v2/check",
headers={"Key": "YOUR_KEY", "Accept": "application/json"},
params={"ipAddress": "1.2.3.4", "maxAgeInDays": 90}
)
data = response.json()["data"]
print(f"Confidence: {data['abuseConfidenceScore']}%, Reports: {data['totalReports']}")
MalwareBazaar (file hashes):
response = requests.post(
"https://mb-api.abuse.ch/api/v1/",
data={"query": "get_info", "hash": sha256_hash}
)
result = response.json()
if result["query_status"] == "ok":
print(result["data"][0]["tags"], result["data"][0]["signature"])
Query MISP for existing events matching the IOC:
from pymisp import PyMISP
misp = PyMISP("https://misp.example.com", "API_KEY")
results = misp.search(value="evil-domain.com", type_attribute="domain")
for event in results:
print(event["Event"]["info"], event["Event"]["threat_level_id"])
Check Shodan for IP context (hosting provider, open ports, banners) to identify if the IP belongs to bulletproof hosting or a legitimate cloud provider (false positive risk).
Apply a tiered decision framework:
Record findings in TIP/MISP with:
Export to STIX indicator object with confidence field set appropriately.
| Term | Definition |
|------|-----------|
| IOC | Indicator of Compromise — observable network or host artifact indicating potential compromise |
| Enrichment | Process of adding contextual data to a raw IOC from multiple intelligence sources |
| Defanging | Modifying IOCs (replacing . with [.]) to prevent accidental activation in documentation |
| False Positive Rate | Percentage of benign artifacts incorrectly flagged as malicious; critical for tuning block thresholds |
| Sinkhole | DNS server redirecting malicious domain lookups to a benign IP for detection without blocking traffic entirely |
| TTL | Time-to-live for an IOC in blocking controls; IP indicators should expire after 30 days, domains after 90 days |
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.