adriannoes/awesome-agentic-ai/building-automated-malware-submission-pipeline

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'Builds an automated malware submission and analysis pipeline that collects

Install

npx vskill@latest install adriannoes/awesome-agentic-ai/building-automated-malware-submission-pipeline

SKILL.md

The same SKILL.md was published earlier as anxious-phyllo879/anthropic-cybersecurity-skills/building-automated-malware-submission-pipeline.

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"

This is the start of the file. Read the full SKILL.md on GitHub.

Security

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  • Model reviewPassed

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