building-automated-malware-submission-pipeline
Helps your agent create an automatic system to send and analyze suspicious files. It collects files, sends them for analysis, and generates reports. Useful for security teams that receive many alerts.
What it does
This tool automates the process of analyzing suspicious files. It collects files from computers and emails, sends them for analysis in secure environments, and checks if they are malicious, generating reports.
When to use it
- ✓When many suspicious file alerts arrive and need quick analysis.
- ✓When manual analysis of suspicious files delays incident response.
- ✓When security tools need automatic verdicts on files.
Example of what to ask your AI
“Automate the analysis of suspicious files collected from our endpoints.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool collects files from sources like EDRs and emails. It then checks if the files are already known in databases like VirusTotal. If not known, it sends them for analysis in 'sandboxes' (secure environments) to determine if they are malicious.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-automated-malware-submission-pipelineOr download just the file:
curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/building-automated-malware-submission-pipeline/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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,
…
FAQ
What is a 'sandbox'?
It's a safe, isolated environment where suspicious files can be run and analyzed without the risk of infecting real systems.
Do I need to pay for any services?
Yes, you need an API key for VirusTotal (can be free for basic lookups or paid for submissions) and access to MalwareBazaar.
Can I use this to analyze malware on work computers?
No, this tool is only for analysis in isolated environments (sandboxes) and never on production systems.
Details
Original description by the author
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. '
Popularity
- GitHub stars
- 35k
- Ranking position
- #5528
- License
- Apache-2.0
- Updated
- 8/31/2026
Comments
- …
More skills from this repository
abusing-dpapi-for-credential-access
Gives your AI agent ready-made instructions for a specific task, so it does it better and faster.
mukul975/Anthropic-Cybersecurity-Skills
abusing-shadow-credentials-for-privesc
Teaches your AI agent to write clear, well-structured text.
mukul975/Anthropic-Cybersecurity-Skills
achieving-cmmc-level-2-compliance
Helps your agent prepare an environment for CMMC Level 2 compliance. It implements security requirements and manages necessary documentation.
mukul975/Anthropic-Cybersecurity-Skills
acquiring-disk-image-with-dd-and-dcfldd
Helps your agent create exact copies of hard drives. This is useful for ensuring no information is lost or altered during investigations.
mukul975/Anthropic-Cybersecurity-Skills
Similar skills
dashboard-builder
Helps your agent create useful monitoring dashboards. It turns data into charts that answer important system questions.
affaan-m/ECC
dev-team
Simulates a collaborative development team where different roles respond to a problem together. It's useful for getting multiple perspectives without manually switching agents.
affaan-m/ECC
ecc-council
Has your agent convene a four-voice council for difficult decisions. It's useful when there are multiple good options and debate is needed before choosing.
affaan-m/ECC