analyzing-indicators-of-compromise
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.
Best use case
analyzing-indicators-of-compromise is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using analyzing-indicators-of-compromise should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/analyzing-indicators-of-compromise/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How analyzing-indicators-of-compromise Compares
| Feature / Agent | analyzing-indicators-of-compromise | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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.Related Skills
performing-active-directory-compromise-investigation
Investigate Active Directory compromise by analyzing authentication logs, replication metadata, Group Policy changes, and Kerberos ticket anomalies to identify attacker persistence and lateral movement paths.
investigating-insider-threat-indicators
Investigates insider threat indicators including data exfiltration attempts, unauthorized access patterns, policy violations, and pre-departure behaviors using SIEM analytics, DLP alerts, and HR data correlation. Use when SOC teams receive insider threat referrals from HR, detect anomalous data movement by employees, or need to build investigation timelines for potential insider threats.
hunting-for-supply-chain-compromise
Hunt for supply chain compromise indicators including trojanized software updates, compromised dependencies, unauthorized code modifications, and tampered build artifacts.
hunting-for-spearphishing-indicators
Hunt for spearphishing campaign indicators across email logs, endpoint telemetry, and network data to detect targeted email attacks.
hunting-for-data-exfiltration-indicators
Hunt for data exfiltration through network traffic analysis, detecting unusual data flows, DNS tunneling, cloud storage uploads, and encrypted channel abuse.
detecting-email-account-compromise
Detect compromised O365 and Google Workspace email accounts by analyzing inbox rule creation, suspicious sign-in locations, mail forwarding rules, and unusual API access patterns via Microsoft Graph and audit logs.
detecting-compromised-cloud-credentials
Detecting compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible travel patterns, unauthorized resource provisioning, and credential abuse indicators using GuardDuty, Defender for Identity, and SCC Event Threat Detection.
detecting-business-email-compromise
Business Email Compromise (BEC) is a sophisticated fraud scheme where attackers impersonate executives, vendors, or trusted partners to trick employees into transferring funds, sharing sensitive data,
detecting-business-email-compromise-with-ai
Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.
collecting-volatile-evidence-from-compromised-host
Collect volatile forensic evidence from a compromised system following order of volatility, preserving memory, network connections, processes, and system state before they are lost.
collecting-indicators-of-compromise
Systematically collects, categorizes, and distributes indicators of compromise (IOCs) during and after security incidents to enable detection, blocking, and threat intelligence sharing. Covers network, host, email, and behavioral indicators using STIX/TAXII formats and threat intelligence platforms. Activates for requests involving IOC collection, indicator extraction, threat indicator sharing, compromise indicators, STIX export, or IOC enrichment.
analyzing-windows-shellbag-artifacts
Analyze Windows Shellbag registry artifacts to reconstruct folder browsing activity, detect access to removable media and network shares, and establish user interaction with directories even after deletion using SBECmd and ShellBags Explorer.