Automates malware submission to sandboxes and generates threat verdicts for security analysis.
Install
mkdir -p .claude/skills/building-automated-malware-submission-pipeline && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/18139" && unzip -o skill.zip -d .claude/skills/building-automated-malware-submission-pipeline && rm skill.zipInstalls to .claude/skills/building-automated-malware-submission-pipeline
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Builds an automated malware submission and analysis pipeline that collectsKey capabilities
- →Collect suspicious files from EDR quarantine APIs
- →Collect attachments from email gateway quarantines
- →Compute MD5, SHA1, and SHA256 hashes for files
- →Pre-screen files using VirusTotal hash lookups
- →Pre-screen files using MalwareBazaar API access
How it works
The skill collects suspicious files from endpoints and email gateways, computes their hashes, and pre-screens them against VirusTotal and MalwareBazaar before potential sandbox submission.
Inputs & outputs
When to use building-automated-malware-submission-pipeline
- →Automate malware triage
- →Scale sandbox submissions
- →Extract indicators of compromise
About this skill
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,pefilelibraries - Isolated analysis network with no production connectivity
Workflow
Step 1: Build File Collection Pipeline
Collect suspicious files from multiple sources:
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:
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:
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
class VerdictGenerator:
def __init__(self):
---
*Content truncated.*
When not to use it
- →Analyzing live malware samples in production environments
- →When isolated sandbox infrastructure is not available
- →When the user wants to perform manual sandbox submissions
Prerequisites
Limitations
- →Not for analyzing live malware samples in production environments
- →Requires isolated analysis network with no production connectivity
- →Requires specific sandbox environments and API keys
How it compares
This skill automates the initial stages of malware analysis by collecting files and performing hash lookups against threat intelligence platforms, reducing manual effort and accelerating triage compared to traditional manual processes.
Compared to similar skills
building-automated-malware-submission-pipeline side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| building-automated-malware-submission-pipeline (this skill) | 0 | 1mo | Review | Advanced |
| senior-security | 31 | 7mo | Review | Advanced |
| telnetshell | 1 | 1mo | Caution | Advanced |
| prowler-sdk-check | 1 | 2mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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