Analyzing Indicators Of CompromiseSAFE

817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains ·

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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 

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

declared (4 observation(s))

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.