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anxious-phyllo879/anthropic-cybersecurity-skills/building-automated-malware-submission-pipeline
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name: building-automated-malware-submission-pipeline description: 'Builds an automated malware submission and analysis pipeline that collects suspicious files from endpoints and email gateways, submits them to sandbox environments and multi-engine scanners, and generates verdicts with IOCs for SIEM integration. Use when SOC teams need to scale malware analysis beyond manual sandbox submissions for high-volume alert triage.

' domain: cybersecurity subdomain: soc-operations tags:

  • soc
  • malware-analysis
  • sandbox
  • automation
  • virustotal
  • cuckoo
  • any-run
  • pipeline

version: '1.0' author: mahipal license: Apache-2.0 nist_csf:

  • DE.CM-01
  • DE.AE-02
  • RS.MA-01
  • DE.AE-06

mitre_attack:

  • T1204.002
  • T1566.001
  • T1027
  • T1055
  • T1497

Building Automated Malware Submission Pipeline

When to Use

Use this skill when:

  • SOC teams face high volume of suspicious file alerts requiring sandbox analysis
  • Manual sandbox submission creates bottlenecks in alert triage workflow
  • Endpoint and email security tools quarantine files needing automated verdict determination
  • Incident response requires rapid malware family identification and IOC extraction

Do not use for analyzing live malware samples in production environments — always use isolated sandbox infrastructure.

Prerequisites

  • Sandbox environment: Cuckoo Sandbox, Joe Sandbox, Any.Run, or VMRay
  • VirusTotal API key (Enterprise for submission, free for lookup)
  • MalwareBazaar API access for known malware lookup
  • File collection mechanism: EDR quarantine API, email gateway export, network capture
  • Python 3.8+ with requests, vt-py, pefile libraries
  • Isolated analysis network with no production connectivity

Workflow

Step 1: Build File Collection Pipeline

Collect suspicious files from multiple sources:

python
import requests
import hashlib
import os
from pathlib import Path
from datetime import datetime
class MalwareCollector:
def __init__(self, quarantine_dir="/opt/malware_quarantine"):
self.quarantine_dir = Path(quarantine_dir)
self.quarantine_dir.mkdir(exist_ok=True)
def collect_from_edr(self, edr_api_url, api_token):
"""Pull quarantined files from CrowdStrike Falcon"""
headers = {"Authorization": f"Bearer {api_token}"}
# Get recent quarantine events
response = requests.get(
f"{edr_api_url}/quarantine/queries/quarantined-files/v1",
headers=headers,
params={"filter": "state:'quarantined'", "limit": 50}
)
file_ids = response.json()["resources"]
for file_id in file_ids:
# Download quarantined file
dl_response = requests.get(
f"{edr_api_url}/quarantine/entities/quarantined-files/v1",
headers=headers,
params={"ids": file_id}
)
file_data = dl_response.content
sha256 = hashlib.sha256(file_data).hexdigest()
filepath = self.quarantine_dir / f"{sha256}.sample"
filepath.write_bytes(file_data)
yield {"sha256": sha256, "path": str(filepath), "source": "edr"}
def collect_from_email_gateway(self, smtp_quarantine_path):
"""Pull attachments from email gateway quarantine"""
import email
from email import policy
for eml_file in Path(smtp_quarantine_path).glob("*.eml"):
msg = email.message_from_binary_file(
eml_file.open("rb"), policy=policy.default
)
for attachment in msg.iter_attachments():
content = attachment.get_content()
if isinstance(content, str):
content = content.encode()
sha256 = hashlib.sha256(content).hexdigest()
filename = attachment.get_filename() or "unknown"
filepath = self.quarantine_dir / f"{sha256}.sample"
filepath.write_bytes(content)
yield {
"sha256": sha256,
"path": str(filepath),
"source": "email",
"original_filename": filename,
"sender": msg["From"],
"subject": msg["Subject"]
}
def compute_hashes(self, filepath):
"""Calculate MD5, SHA1, SHA256 for a file"""
with open(filepath, "rb") as f:
content = f.read()
return {
"md5": hashlib.md5(content).hexdigest(),
"sha1": hashlib.sha1(content).hexdigest(),
"sha256": hashlib.sha256(content).hexdigest(),
"size": len(content)
}

Step 2: Pre-Screen with Hash Lookups

Check if the file is already known before sandbox submission:

python
import vt
class MalwarePreScreener:
def __init__(self, vt_api_key, mb_api_url="https://mb-api.abuse.ch/api/v1/"):
self.vt_client = vt.Client(vt_api_key)
self.mb_api_url = mb_api_url
def check_virustotal(self, sha256):
"""Lookup hash in VirusTotal"""
try:
file_obj = self.vt_client.get_object(f"/files/{sha256}")
stats = file_obj.last_analysis_stats
return {
"found": True,
"malicious": stats.get("malicious", 0),
"suspicious": stats.get("suspicious", 0),
"undetected": stats.get("undetected", 0),
"total": sum(stats.values()),
"threat_label": getattr(file_obj, "popular_threat_classification", {}).get(
"suggested_threat_label", "Unknown"
),
"type": getattr(file_obj, "type_description", "Unknown")
}
except vt.APIError:
return {"found": False}
def check_malwarebazaar(self, sha256):
"""Lookup hash in MalwareBazaar"""
response = requests.post(
self.mb_api_url,
data={"query": "get_info", "hash": sha256}
)
data = response.json()
if data["query_status"] == "ok":
entry = data["data"][0]
return {
"found": True,
"signature": entry.get("signature", "Unknown"),
"tags": entry.get("tags", []),
"file_type": entry.get("file_type", "Unknown"),
"first_seen": entry.get("first_seen", "Unknown")
}
return {"found": False}
def pre_screen(self, sha256):
"""Run all pre-screening checks"""
vt_result = self.check_virustotal(sha256)
mb_result = self.check_malwarebazaar(sha256)
verdict = "UNKNOWN"
if vt_result["found"] and vt_result.get("malicious", 0) > 10:
verdict = "KNOWN_MALICIOUS"
elif vt_result["found"] and vt_result.get("malicious", 0) == 0:
verdict = "LIKELY_CLEAN"
return {
"sha256": sha256,
"virustotal": vt_result,
"malwarebazaar": mb_result,
"pre_screen_verdict": verdict,
"needs_sandbox": verdict == "UNKNOWN"
}
def close(self):
self.vt_client.close()

Step 3: Submit to Sandbox for Dynamic Analysis

Cuckoo Sandbox Submission:

python
class SandboxSubmitter:
def __init__(self, cuckoo_url="http://cuckoo.internal:8090"):
self.cuckoo_url = cuckoo_url
def submit_to_cuckoo(self, filepath, timeout=300):
"""Submit file to Cuckoo Sandbox"""
with open(filepath, "rb") as f:
response = requests.post(
f"{self.cuckoo_url}/tasks/create/file",
files={"file": f},
data={
"timeout": timeout,
"options": "procmemdump=yes,route=none",
"priority": 2,
"machine": "win10_x64"
}
)
task_id = response.json()["task_id"]
return task_id
def wait_for_analysis(self, task_id, poll_interval=30, max_wait=600):
"""Wait for sandbox analysis to complete"""
import time
elapsed = 0
while elapsed < max_wait:
response = requests.get(f"{self.cuckoo_url}/tasks/view/{task_id}")
status = response.json()["task"]["status"]
if status == "reported":
return self.get_report(task_id)
elif status == "failed_analysis":
return {"error": "Analysis failed"}
time.sleep(poll_interval)
elapsed += poll_interval
return {"error": "Analysis timed out"}
def get_report(self, task_id):
"""Retrieve analysis report"""
response = requests.get(f"{self.cuckoo_url}/tasks/report/{task_id}")
report = response.json()
# Extract key indicators
return {
"task_id": task_id,
"score": report.get("info", {}).get("score", 0),
"signatures": [
{"name": s["name"], "severity": s["severity"], "description": s["description"]}
for s in report.get("signatures", [])
],
"network": {
"dns": [d["request"] for d in report.get("network", {}).get("dns", [])],
"http": [
{"url": h["uri"], "method": h["method"]}
for h in report.get("network", {}).get("http", [])
],
"hosts": report.get("network", {}).get("hosts", [])
},
"dropped_files": [
{"name": f["name"], "sha256": f["sha256"], "size": f["size"]}
for f in report.get("dropped", [])
],
"processes": [
{"name": p["process_name"], "pid": p["pid"], "command_line": p.get("command_line", "")}
for p in report.get("behavior", {}).get("processes", [])
],
"registry_keys": [
k for k in report.get("behavior", {}).get("summary", {}).get("regkey_written", [])
]
}
def submit_to_joesandbox(self, filepath, joe_api_key, joe_url="https://jbxcloud.joesecurity.org/api"):
"""Submit to Joe Sandbox Cloud"""
with open(filepath, "rb") as f:
response = requests.post(
f"{joe_url}/v2/submission/new",
headers={"Authorization": f"Bearer {joe_api_key}"},
files={"sample": f},
data={
"systems": "w10_64",
"internet-access": False,
"report-cache": True
}
)
return response.json()["data"]["webid"]

Step 4: Extract IOCs and Generate Verdict

python
class VerdictGenerator:
def __init__(self):
self.malicious_threshold = 7 # Cuckoo score threshold
def generate_verdict(self, pre_screen, sandbox_report):
"""Combine pre-screening and sandbox results for final verdict"""
iocs = {
"ips": [],
"domains": [],
"urls": [],
"hashes": [],
"registry_keys": [],
"files_dropped": []
}
# Extract IOCs from sandbox report
if sandbox_report:
iocs["domains"] = sandbox_report.get("network", {}).get("dns", [])
iocs["ips"] = sandbox_report.get("network", {}).get("hosts", [])
iocs["urls"] = [
h["url"] for h in sandbox_report.get("network", {}).get("http", [])
]
iocs["hashes"] = [
f["sha256"] for f in sandbox_report.get("dropped_files", [])
]
iocs["registry_keys"] = sandbox_report.get("registry_keys", [])[:10]
iocs["files_dropped"] = sandbox_report.get("dropped_files", [])
# Determine verdict
vt_malicious = pre_screen.get("virustotal", {}).get("malicious", 0)
sandbox_score = sandbox_report.get("score", 0) if sandbox_report else 0
sig_count = len(sandbox_report.get("signatures", [])) if sandbox_report else 0
combined_score = (vt_malicious * 2) + (sandbox_score * 10) + (sig_count * 5)
if combined_score >= 100:
verdict = "MALICIOUS"
confidence = "HIGH"
elif combined_score >= 50:
verdict = "SUSPICIOUS"
confidence = "MEDIUM"
elif combined_score >= 20:
verdict = "POTENTIALLY_UNWANTED"
confidence = "LOW"
else:
verdict = "CLEAN"
confidence = "HIGH"
return {
"verdict": verdict,
"confidence": confidence,
"combined_score": combined_score,
"iocs": iocs,
"vt_detections": vt_malicious,
"sandbox_score": sandbox_score,
"signatures": sandbox_report.get("signatures", []) if sandbox_report else []
}

Step 5: Push Results to SIEM

python
def push_to_splunk(verdict_result, splunk_url, splunk_token):
"""Send malware analysis verdict to Splunk HEC"""
import json
event = {
"sourcetype": "malware_analysis",
"source": "malware_pipeline",
"event": {
"sha256": verdict_result["sha256"],
"verdict": verdict_result["verdict"],
"confidence": verdict_result["confidence"],
"score": verdict_result["combined_score"],
"vt_detections": verdict_result["vt_detections"],
"sandbox_score": verdict_result["sandbox_score"],
"malware_family": verdict_result.get("threat_label", "Unknown"),
"iocs": verdict_result["iocs"],
"signatures": [s["name"] for s in verdict_result["signatures"]]
}
}
response = requests.post(
f"{splunk_url}/services/collector/event",
headers={
"Authorization": f"Splunk {splunk_token}",
"Content-Type": "application/json"
},
json=event,
verify=not os.environ.get("SKIP_TLS_VERIFY", "").lower() == "true", # Set SKIP_TLS_VERIFY=true for self-signed certs in lab environments
)
return response.status_code == 200
def push_iocs_to_blocklist(iocs, firewall_api):
"""Push extracted IOCs to blocking infrastructure"""
for ip in iocs.get("ips", []):
requests.post(
f"{firewall_api}/block",
json={"type": "ip", "value": ip, "action": "block", "source": "malware_pipeline"}
)
for domain in iocs.get("domains", []):
requests.post(
f"{firewall_api}/block",
json={"type": "domain", "value": domain, "action": "sinkhole", "source": "malware_pipeline"}
)

Step 6: Orchestrate the Full Pipeline

python
def run_malware_pipeline(sample_path, config):
"""Execute full malware analysis pipeline"""
collector = MalwareCollector()
screener = MalwarePreScreener(config["vt_key"])
submitter = SandboxSubmitter(config["cuckoo_url"])
generator = VerdictGenerator()
# Step 1: Hash and pre-screen
hashes = collector.compute_hashes(sample_path)
pre_screen = screener.pre_screen(hashes["sha256"])
# Step 2: Submit to sandbox if unknown
sandbox_report = None
if pre_screen["needs_sandbox"]:
task_id = submitter.submit_to_cuckoo(sample_path)
sandbox_report = submitter.wait_for_analysis(task_id)
# Step 3: Generate verdict
verdict = generator.generate_verdict(pre_screen, sandbox_report)
verdict["sha256"] = hashes["sha256"]
verdict["threat_label"] = pre_screen.get("virustotal", {}).get("threat_label", "Unknown")
# Step 4: Push to SIEM
push_to_splunk(verdict, config["splunk_url"], config["splunk_token"])
# Step 5: Block if malicious
if verdict["verdict"] == "MALICIOUS":
push_iocs_to_blocklist(verdict["iocs"], config["firewall_api"])
screener.close()
return verdict

Key Concepts

TermDefinition
Dynamic AnalysisExecuting malware in a sandbox to observe runtime behavior (process creation, network, file system changes)
Static AnalysisExamining malware without execution (hash lookup, string analysis, PE header inspection)
Sandbox EvasionTechniques malware uses to detect sandbox environments and alter behavior to avoid analysis
IOC ExtractionAutomated process of identifying network indicators, file artifacts, and registry changes from sandbox reports
Multi-AV ScanningSubmitting samples to multiple antivirus engines (VirusTotal) for consensus-based detection
VerdictFinal classification of a sample: Malicious, Suspicious, Potentially Unwanted, or Clean

Tools & Systems

  • Cuckoo Sandbox: Open-source automated malware analysis platform with behavioral analysis and network capture
  • Joe Sandbox: Commercial sandbox with deep behavioral analysis, YARA matching, and MITRE ATT&CK mapping
  • Any.Run: Interactive sandbox service allowing real-time manipulation during analysis for debugging evasive malware
  • VirusTotal: Multi-engine scanning service providing 70+ AV results and behavioral analysis reports
  • CAPE Sandbox: Community-maintained Cuckoo fork with enhanced payload extraction and configuration dumping

Common Scenarios

  • Email Attachment Triage: Auto-submit quarantined email attachments, generate verdict in <5 minutes
  • EDR Quarantine Processing: Batch-process files quarantined by endpoint security for detailed analysis
  • Incident Investigation: Submit suspicious binaries found during IR for malware family identification and IOC extraction
  • Threat Intel Enrichment: Analyze samples from threat feeds to extract C2 infrastructure and update blocking
  • Zero-Day Detection: Sandbox catches novel malware missed by signature-based AV through behavioral analysis

Output Format

MALWARE ANALYSIS REPORT — Pipeline Submission
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Sample: invoice_march.docx
SHA256: a1b2c3d4e5f6a7b8...
File Type: Microsoft Word Document (macro-enabled)
Pre-Screening:
VirusTotal: 34/72 malicious (Emotet.Downloader)
MalwareBazaar: Tags: emotet, macro, downloader
Sandbox Analysis (Cuckoo):
Score: 9.2/10 (MALICIOUS)
Signatures:
- Macro executes PowerShell download cradle (severity: 8)
- Process injection into explorer.exe (severity: 9)
- Connects to known Emotet C2 server (severity: 9)
Extracted IOCs:
C2 IPs: 185.234.218[.]50:8080, 45.77.123[.]45:443
Domains: update-service[.]evil[.]com
Dropped Files: payload.dll (SHA256: b2c3d4e5...)
Registry: HKCU\Software\Microsoft\Windows\CurrentVersion\Run\Update
VERDICT: MALICIOUS (Emotet Downloader) — Confidence: HIGH
ACTIONS:
[DONE] IOCs pushed to Splunk threat intel
[DONE] C2 IPs blocked on firewall
[DONE] Domain sinkholed on DNS
[DONE] Hash blocked on endpoint
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