analyzing-indicators-of-compromise — Claude Code skill: IOC triage
Scores IPs, domains, hashes, URLs and email artifacts for maliciousness, then sorts them into block, monitor or whitelist.
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-indicators-of-compromise -a claude-codeWhat analyzing-indicators-of-compromise does
The skill walks an indicator through a fixed order. First it normalizes and classifies each IOC: private RFC 1918 addresses get skipped, domains and URLs get defanged for safe handling, file hashes get typed by MD5, SHA-1 or SHA-256. Then comes enrichment from several sources at once.
VirusTotal covers hashes, URLs, IPs and domains. AbuseIPDB handles IP reputation, MalwareBazaar checks hashes against known malware families, MISP searches for matching campaign events, and Shodan adds hosting context so you can tell bulletproof hosting from a legitimate cloud provider. Ready-made Python snippets sit inside: vt-py for VirusTotal, requests for AbuseIPDB and MalwareBazaar, pymisp for MISP. You need API keys for VirusTotal and AbuseIPDB, plus a MISP instance or TIP to cross-reference.
Disposition follows a tiered framework: high confidence means block, medium means monitor, low means investigate or whitelist. The author is explicit that this should not run alone on high-stakes blocking calls — shared infrastructure like CDNs and cloud providers produces false positives, so automated enrichment needs an analyst on top.
What it helps with
- triage a batch of IOCs pulled from a phishing email
- score an IP or domain before adding it to a blocklist
- check a file hash against MalwareBazaar for a malware family
- link an indicator to a known campaign through MISP
- filter out false positives on CDN and cloud provider IPs
Example prompts for the agent
- Triage these IOCs from the phishing email: domain, URL and attachment hash — block or monitor?
- Check IP 1.2.3.4 against AbuseIPDB and Shodan: bulletproof hosting or a normal cloud provider?
- Search MISP for events matching this domain and tell me which campaign it belongs to
How to install analyzing-indicators-of-compromise
1Skills CLI (Vercel)
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-indicators-of-compromise -a claude-code2Manually: put the files in place
~/.claude/skills/analyzing-indicators-of-compromise/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/analyzing-indicators-of-compromise/
1Skills CLI (Vercel)
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-indicators-of-compromise -a codex2Manually: put the files in place
.agents/skills/analyzing-indicators-of-compromise/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-indicators-of-compromise -a cursor2Manually: put the files in place
.cursor/skills/analyzing-indicators-of-compromise/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-indicators-of-compromise2Manually: put the files in place
.github/skills/analyzing-indicators-of-compromise/SKILL.md1Skills CLI (Vercel)
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-indicators-of-compromise -a gemini-cli2Platform CLI
gemini skills install https://github.com/mukul975/Anthropic-Cybersecurity-Skills --path skills/analyzing-indicators-of-compromiseManually: put the files in place
.devin/skills/analyzing-indicators-of-compromise/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-indicators-of-compromise -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about analyzing-indicators-of-compromise
How do I install analyzing-indicators-of-compromise in Claude?
Run npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-indicators-of-compromise -a claude-code. The skill file then lives at ~/.claude/skills/analyzing-indicators-of-compromise/SKILL.md.
What does analyzing-indicators-of-compromise do?
It classifies IOCs, enriches them from VirusTotal, AbuseIPDB, MalwareBazaar and MISP, then assigns a confidence score and a disposition: block, monitor or whitelist.
What do I need before using it?
A VirusTotal API key, an AbuseIPDB API key and a MISP instance or TIP. The bundled snippets are Python and rely on requests, vt-py and pymisp.
Source text
SKILL.md · Apache-2.0
---
name: analyzing-indicators-of-compromise
description: '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.
'
domain: cybersecurity
subdomain: threat-intelligence
tags:
- IOC
- VirusTotal
- AbuseIPDB
- MalwareBazaar
- MISP
- threat-intelligence
- STIX
- NIST-CSF
version: 1.0.0
author: mahipal
license: Apache-2.0
atlas_techniques:
- AML.T0052
nist_csf:
- ID.RA-01
- ID.RA-05
- DE.CM-01
- DE.AE-02
mitre_attack:
- T1071
- T1105
- T1041
- T1567
mitre_f3:
version: '1.1'
tactics:
- reconnaissance
- resource-development
- initial-access
techniques:
- id: T1598
name: Phishing for Information
tactic: reconnaissance
source: attack
- id: T1660
name: Phishing
tactic: initial-access
source: attack
- id: T1583.001
name: 'Acquire Infrastructure: Domains'
tactic: resource-development
source: attack
- id: F1020.002
name: 'Create Fake Materials: Fake Website'
tactic: resource-development
source: f3
---
# Analyzing Indicators of Compromise
## When to Use
Use this skill when:
- A phishing email or alert generates IOCs (URLs, IP addresses, file hashes) requiring rapid triage
- Automated feeds deliver bulk IOCs that need confidence scoring before ingestion into blocking controls
- An incident investigation requires contextual enrichment of observed network artifacts
**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).
## Prerequisites
- VirusTotal API key (free or Enterprise) for multi-AV and sandbox lookup
- AbuseIPDB API key for IP reputation checks
- MISP instance or TIP for cross-referencing against known campaigns
- Python with `requests` and `vt-py` libraries, or SOAR platform with pre-built connectors
## Workflow
### Step 1: Normalize and Classify IOC Types
Before enriching, classify each IOC:
- **IPv4/IPv6 address**: Check if RFC 1918 private (skip external enrichment), validate format
- **Domain/FQDN**: Defang for safe handling (`evil[.]com`), extract registered domain via tldextract
- **URL**: Extract domain + path separately; check for redirectors
- **File hash**: Identify hash type (MD5/SHA-1/SHA-256); prefer SHA-256 for uniqueness
- **Email address**: Split into domain (check MX/DMARC) and local part for pattern analysis
Defang IOCs in documentation (replace `.` with `[.]` and `://` with `[://]`) to prevent accidental clicks.
### Step 2: Multi-Source Enrichment
**VirusTotal (file hash, URL, IP, domain)**:
```python
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)**:
```python
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)**:
```python
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"])
```
### Step 3: Contextualize with Campaign Attribution
Query MISP for existing events matching the IOC:
```python
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).
### Step 4: Assign Confidence Score and Disposition
Apply a tiered decision framework:
- **Block (High Confidence ≥ 70%)**: ≥15 AV detections on VT, AbuseIPDB score ≥70, matches known malware family or campaign
- **Monitor/Alert (Medium 40–69%)**: 5–14 AV detections, moderate AbuseIPDB score, no campaign attribution
- **Whitelist/Investigate (Low <40%)**: ≤4 AV detections, no abuse reports, legitimate service (Google, Cloudflare CDN IPs)
- **False Positive**: Legitimate business service incorrectly flagged; document and exclude from future alerts
### Step 5: Document and Distribute
Record findings in TIP/MISP with:
- All enrichment data collected (timestamps, source, score)
- Disposition decision and rationale
- Blocking actions taken (firewall, proxy, DNS sinkhole)
- Related incident ticket number
Export to STIX indicator object with confidence field set appropriately.
## Key Concepts
| 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 & Systems
- **VirusTotal**: Multi-engine malware scanner and threat intelligence platform with 70+ AV engines, sandbox reports, and community comments
- **AbuseIPDB**: Community-maintained IP reputation database with 90-day abuse report history
- **MalwareBazaar (abuse.ch)**: Free malware hash repository with YARA rule associations and malware family tagging
- **URLScan.io**: Free URL analysis service that captures screenshots, DOM, and network requests for phishing URL triage
- **Shodan**: Internet-wide scan data providing hosting provider, open ports, and banner information for IP enrichment
## Common Pitfalls
- **Blocking shared infrastructure**: CDN IPs (Cloudflare 104.21.x.x, AWS CloudFront) may legitimately host malicious content but blocking the IP disrupts thousands of legitimate sites.
- **VT score obsession**: Low VT detection count does not mean benign — zero-day malware and custom APT tools often score 0 initially. Check sandbox behavior, MISP, and passive DNS.
- **Missing defanging**: Pasting live IOCs in emails or Confluence docs can trigger automated URL scanners or phishing tools.
- **No expiration policy**: IOCs without TTLs accumulate in blocklists indefinitely, generating false positives as infrastructure is repurposed by legitimate users.
- **Over-relying on single source**: VirusTotal aggregates AV opinions — all may be wrong or lag behind emerging malware. Use 3+ independent sources for high-stakes decisions.
© mahipal · Apache-2.0 · mukul975/Anthropic-Cybersecurity-Skills