performing-malware-hash-enrichment-with-virustotal
Enrich malware file hashes using the VirusTotal API to retrieve detection rates, behavioral analysis, YARA matches, and contextual threat intelligence for incident triage and IOC validation.
Best use case
performing-malware-hash-enrichment-with-virustotal is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Enrich malware file hashes using the VirusTotal API to retrieve detection rates, behavioral analysis, YARA matches, and contextual threat intelligence for incident triage and IOC validation.
Teams using performing-malware-hash-enrichment-with-virustotal 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/performing-malware-hash-enrichment-with-virustotal/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How performing-malware-hash-enrichment-with-virustotal Compares
| Feature / Agent | performing-malware-hash-enrichment-with-virustotal | 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?
Enrich malware file hashes using the VirusTotal API to retrieve detection rates, behavioral analysis, YARA matches, and contextual threat intelligence for incident triage and IOC validation.
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
# Performing Malware Hash Enrichment with VirusTotal
## Overview
VirusTotal is the world's largest crowdsourced malware corpus, scanning files with 70+ antivirus engines and providing behavioral analysis, YARA rule matches, network indicators, and community intelligence. This skill covers using the VirusTotal API v3 to enrich file hashes (MD5, SHA-1, SHA-256) with detection verdicts, sandbox reports, related indicators, and contextual intelligence for SOC triage, incident response, and threat intelligence enrichment workflows.
## When to Use
- When conducting security assessments that involve performing malware hash enrichment with virustotal
- When following incident response procedures for related security events
- When performing scheduled security testing or auditing activities
- When validating security controls through hands-on testing
## Prerequisites
- Python 3.9+ with `vt-py` (official VirusTotal Python client) or `requests`
- VirusTotal API key (free tier: 4 requests/minute, 500/day; premium for higher limits)
- Understanding of file hash types: MD5, SHA-1, SHA-256
- Familiarity with AV detection naming conventions
- STIX 2.1 knowledge for IOC representation
## Key Concepts
### VirusTotal API v3
The API provides RESTful endpoints for file reports (`/files/{hash}`), URL scanning, domain reports, IP address intelligence, and advanced hunting with VirusTotal Intelligence (VTI). Each file report includes detection results from 70+ AV engines, behavioral analysis from sandboxes, YARA rule matches, sigma rule matches, file metadata (PE headers, imports, sections), network indicators (contacted IPs, domains, URLs), and community votes and comments.
### Hash Enrichment Workflow
The typical enrichment flow is: receive hash from alert/EDR -> query VT API -> parse detection ratio -> extract behavioral indicators -> correlate with existing intelligence -> make triage decision. The API returns a `last_analysis_stats` object with `malicious`, `suspicious`, `undetected`, and `harmless` counts.
### Pivoting from Hashes
VirusTotal enables pivoting from a single hash to related intelligence: similar files (ITW/in-the-wild samples), contacted domains and IPs (C2 infrastructure), dropped files, embedded URLs, YARA rule matches, and threat actor attribution through crowdsourced intelligence.
## Workflow
### Step 1: Query VirusTotal for Hash Report
```python
import vt
import json
import hashlib
from datetime import datetime
class VTEnricher:
def __init__(self, api_key):
self.client = vt.Client(api_key)
def enrich_hash(self, file_hash):
"""Enrich a file hash with VirusTotal intelligence."""
try:
file_obj = self.client.get_object(f"/files/{file_hash}")
stats = file_obj.last_analysis_stats
report = {
"hash": file_hash,
"sha256": file_obj.sha256,
"sha1": file_obj.sha1,
"md5": file_obj.md5,
"file_type": getattr(file_obj, "type_description", "Unknown"),
"file_size": getattr(file_obj, "size", 0),
"first_submission": str(getattr(file_obj, "first_submission_date", "")),
"last_analysis_date": str(getattr(file_obj, "last_analysis_date", "")),
"detection_stats": {
"malicious": stats.get("malicious", 0),
"suspicious": stats.get("suspicious", 0),
"undetected": stats.get("undetected", 0),
"harmless": stats.get("harmless", 0),
},
"detection_ratio": f"{stats.get('malicious', 0)}/{sum(stats.values())}",
"popular_threat_names": getattr(file_obj, "popular_threat_classification", {}),
"tags": getattr(file_obj, "tags", []),
"names": getattr(file_obj, "names", []),
}
total_engines = sum(stats.values())
mal_count = stats.get("malicious", 0)
report["threat_level"] = (
"critical" if mal_count > total_engines * 0.7
else "high" if mal_count > total_engines * 0.4
else "medium" if mal_count > total_engines * 0.1
else "low" if mal_count > 0
else "clean"
)
print(f"[+] {file_hash[:16]}... -> {report['detection_ratio']} "
f"({report['threat_level'].upper()})")
return report
except vt.error.APIError as e:
print(f"[-] VT API error for {file_hash}: {e}")
return None
def get_behavior_report(self, file_hash):
"""Get sandbox behavioral analysis for a file."""
try:
behaviors = self.client.get_object(f"/files/{file_hash}/behaviours")
behavior_data = {
"processes_created": [],
"files_written": [],
"registry_keys_set": [],
"dns_lookups": [],
"http_conversations": [],
"mutexes_created": [],
"commands_executed": [],
}
for sandbox in getattr(behaviors, "data", []):
attrs = sandbox.get("attributes", {})
behavior_data["processes_created"].extend(
attrs.get("processes_created", []))
behavior_data["files_written"].extend(
[f.get("path", "") for f in attrs.get("files_written", [])])
behavior_data["registry_keys_set"].extend(
[r.get("key", "") for r in attrs.get("registry_keys_set", [])])
behavior_data["dns_lookups"].extend(
[d.get("hostname", "") for d in attrs.get("dns_lookups", [])])
behavior_data["commands_executed"].extend(
attrs.get("command_executions", []))
return behavior_data
except Exception as e:
print(f"[-] Behavior report error: {e}")
return {}
def close(self):
self.client.close()
# Usage
enricher = VTEnricher("YOUR_VT_API_KEY")
report = enricher.enrich_hash("275a021bbfb6489e54d471899f7db9d1663fc695ec2fe2a2c4538aabf651fd0f")
print(json.dumps(report, indent=2, default=str))
enricher.close()
```
### Step 2: Batch Hash Enrichment with Rate Limiting
```python
import time
import csv
def batch_enrich(api_key, hash_file, output_file, rate_limit=4):
"""Enrich a list of hashes from a file with rate limiting."""
enricher = VTEnricher(api_key)
results = []
with open(hash_file, "r") as f:
hashes = [line.strip() for line in f if line.strip()]
print(f"[*] Enriching {len(hashes)} hashes (rate: {rate_limit}/min)")
for i, file_hash in enumerate(hashes):
report = enricher.enrich_hash(file_hash)
if report:
results.append(report)
if (i + 1) % rate_limit == 0:
print(f" [{i+1}/{len(hashes)}] Rate limit pause (60s)...")
time.sleep(60)
# Export to CSV
with open(output_file, "w", newline="") as f:
if results:
writer = csv.DictWriter(f, fieldnames=results[0].keys())
writer.writeheader()
for r in results:
flat = {k: str(v) for k, v in r.items()}
writer.writerow(flat)
print(f"[+] Enrichment complete: {len(results)}/{len(hashes)} hashes")
print(f"[+] Results saved to {output_file}")
enricher.close()
return results
batch_enrich("YOUR_API_KEY", "hashes.txt", "enrichment_results.csv")
```
### Step 3: Extract Network Indicators for Pivoting
```python
def extract_network_iocs(api_key, file_hash):
"""Extract network-based IOCs from VT for C2 identification."""
client = vt.Client(api_key)
network_iocs = {
"contacted_ips": [],
"contacted_domains": [],
"contacted_urls": [],
"embedded_urls": [],
}
try:
# Get contacted IPs
it = client.iterator(f"/files/{file_hash}/contacted_ips")
for ip_obj in it:
network_iocs["contacted_ips"].append({
"ip": ip_obj.id,
"country": getattr(ip_obj, "country", ""),
"asn": getattr(ip_obj, "asn", 0),
"as_owner": getattr(ip_obj, "as_owner", ""),
})
# Get contacted domains
it = client.iterator(f"/files/{file_hash}/contacted_domains")
for domain_obj in it:
network_iocs["contacted_domains"].append({
"domain": domain_obj.id,
"registrar": getattr(domain_obj, "registrar", ""),
"creation_date": str(getattr(domain_obj, "creation_date", "")),
})
# Get contacted URLs
it = client.iterator(f"/files/{file_hash}/contacted_urls")
for url_obj in it:
network_iocs["contacted_urls"].append({
"url": url_obj.url,
"last_http_response_code": getattr(url_obj, "last_http_response_content_length", 0),
})
except Exception as e:
print(f"[-] Error extracting network IOCs: {e}")
finally:
client.close()
print(f"[+] Network IOCs: {len(network_iocs['contacted_ips'])} IPs, "
f"{len(network_iocs['contacted_domains'])} domains, "
f"{len(network_iocs['contacted_urls'])} URLs")
return network_iocs
```
### Step 4: YARA Rule Matching and Threat Classification
```python
def get_yara_matches(api_key, file_hash):
"""Retrieve YARA rule matches for threat classification."""
client = vt.Client(api_key)
try:
file_obj = client.get_object(f"/files/{file_hash}")
crowdsourced_yara = getattr(file_obj, "crowdsourced_yara_results", [])
matches = []
for rule in crowdsourced_yara:
matches.append({
"rule_name": rule.get("rule_name", ""),
"ruleset_name": rule.get("ruleset_name", ""),
"author": rule.get("author", ""),
"description": rule.get("description", ""),
"source": rule.get("source", ""),
})
# Classify based on YARA matches
classifications = set()
for m in matches:
rule_lower = m["rule_name"].lower()
if any(k in rule_lower for k in ["apt", "nation", "state"]):
classifications.add("apt")
if any(k in rule_lower for k in ["ransom", "crypto"]):
classifications.add("ransomware")
if any(k in rule_lower for k in ["trojan", "rat", "backdoor"]):
classifications.add("trojan")
if any(k in rule_lower for k in ["loader", "dropper"]):
classifications.add("loader")
print(f"[+] YARA: {len(matches)} rules matched")
print(f"[+] Classifications: {classifications or {'unclassified'}}")
return {"matches": matches, "classifications": list(classifications)}
finally:
client.close()
```
### Step 5: Generate Enrichment Report
```python
def generate_enrichment_report(hash_report, behavior, network, yara_data):
"""Generate comprehensive enrichment report."""
report = {
"metadata": {
"generated": datetime.now().isoformat(),
"hash": hash_report.get("sha256", ""),
},
"verdict": {
"threat_level": hash_report.get("threat_level", "unknown"),
"detection_ratio": hash_report.get("detection_ratio", "0/0"),
"classifications": yara_data.get("classifications", []),
"threat_names": hash_report.get("popular_threat_names", {}),
},
"behavioral_indicators": {
"processes": behavior.get("processes_created", [])[:10],
"dns_queries": behavior.get("dns_lookups", [])[:10],
"commands": behavior.get("commands_executed", [])[:10],
},
"network_indicators": {
"c2_candidates": network.get("contacted_ips", [])[:10],
"domains": network.get("contacted_domains", [])[:10],
},
"yara_matches": yara_data.get("matches", [])[:10],
"recommendation": (
"BLOCK and investigate" if hash_report.get("threat_level") in ("critical", "high")
else "Monitor and analyze" if hash_report.get("threat_level") == "medium"
else "Low risk - continue monitoring"
),
}
with open(f"enrichment_{hash_report.get('sha256', 'unknown')[:16]}.json", "w") as f:
json.dump(report, f, indent=2, default=str)
return report
```
## Validation Criteria
- VT API v3 queried successfully with proper authentication
- File hash enriched with detection stats, behavioral data, and network indicators
- Batch enrichment handles rate limiting correctly
- Network IOCs extracted for C2 identification
- YARA matches retrieved and used for classification
- Enrichment report generated with actionable verdict
## References
- [VirusTotal API v3 Documentation](https://docs.virustotal.com/reference/overview)
- [vt-py Official Python Client](https://github.com/VirusTotal/vt-py)
- [VirusTotal Intelligence](https://www.virustotal.com/gui/intelligence-overview)
- [Torq: VT Hash Enrichment Workflow](https://kb.torq.io/en/articles/9350251-virustotal-file-hash-enrichment-with-cache-workflow-template)
- [Dynatrace: Enrich Observables with VT](https://www.dynatrace.com/news/blog/enrich-observables-with-virustotal-threat-intelligence/)
- [Penligent: VT in Incident Response](https://www.penligent.ai/hackinglabs/virustotal-in-incident-response-how-to-identify-malware-fast-and-pivot-without-leaking-data/)Related Skills
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