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 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

1. Isolate the sample — ensure the malware is in a sandboxed environment with no network access
2. Record file metadata — hash the sample and note file type, size, and compile timestamp
3. Static analysis — examine strings, imports, and disassembled code without execution
4. Dynamic analysis — execute in a monitored sandbox and record behavior (file, registry, network)
5. Document IOCs — extract indicators of compromise and write the analysis report

Step 1: Query VirusTotal for Hash Report

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

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

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

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

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

When NOT to Use

• You don't have explicit written authorization to test
• Task is about defense/detection, not offense (use detection skills)
• You need to implement security controls (use implementing-* skills)
• Task requires compliance auditing (use auditing-* skills)
• You're investigating an incident (use incident response skills)
• Target is out of scope for your engagement
• Task is about vulnerability scanning only (use scanning tools)

Red Flags

• Performing actions without explicit written authorization from the asset owner
• Testing against production systems without a defined scope and rules of engagement
• Analyzing malware on a machine connected to the production network
• Failing to isolate the analysis environment from the internet
• Executing samples without proper containment (VM, sandbox)

Verification

• All steps executed successfully against a test environment before production use
• Output documented with screenshots or logs demonstrating expected behavior
• Sample hash recorded and verified (MD5, SHA-1, SHA-256)
• Analysis environment confirmed isolated from production network
• Indicators of compromise (IOCs) extracted and documented

References

• VirusTotal API v3 Documentation
• vt-py Official Python Client
• VirusTotal Intelligence
• Torq: VT Hash Enrichment Workflow
• Dynatrace: Enrich Observables with VT
• Penligent: VT in Incident Response

Process

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality

Anti-Rationalization Table