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. Use when working with performing malware hash enrichment with virustotal.
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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. Use when working with performing malware hash enrichment with virustotal.
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
Trigger phrases:
"performing malware hash enrichment with virustotal"
"Enrich malware file hashes using the VirusTotal API to retrieve detection rates,"
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
This section covers key concepts for performing malware hash enrichment with virustotal.
Ensure all prerequisites are met before proceeding
Follow the documented workflow steps in sequence
Record results and any anomalies encountered during this phase
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 , , , and counts.
malicious
suspicious
undetected
harmless
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
Isolate the sample — ensure the malware is in a sandboxed environment with no network access
Record file metadata — hash the sample and note file type, size, and compile timestamp
Static analysis — examine strings, imports, and disassembled code without execution
Dynamic analysis — execute in a monitored sandbox and record behavior (file, registry, network)
Document IOCs — extract indicators of compromise and write the analysis report
import time
import csv
defbatch_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 = []
withopen(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 inenumerate(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 CSVwithopen(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
defextract_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
defget_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()
ifany(k in rule_lower for k in ["apt", "nation", "state"]):
classifications.add("apt")
ifany(k in rule_lower for k in ["ransom", "crypto"]):
classifications.add("ransomware")
ifany(k in rule_lower for k in ["trojan", "rat", "backdoor"]):
classifications.add("trojan")
ifany(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()
