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building-automated-malware-submission-pipeline

'Builds an automated malware submission and analysis pipeline that collects

Install / Use

npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-automated-malware-submission-pipeline

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

96/100

Category

Security

Supported Platforms

Universal

Our assessment of building-automated-malware-submission-pipeline

building-automated-malware-submission-pipeline scores 96/100 on our quality scale, 101st of 544 Security skills we index (top 19%).

Its SKILL.md is 18 KB long, well organised into 14 sections with 7 code examples: a thorough specification that gives an agent plenty to work with.

With 33,340 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
30/30
Structure
20/20
Description
12/15
Adoption
19/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 25 days ago, so building-automated-malware-submission-pipeline is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-25. Automated pattern scan on 2026-09-25. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

building-automated-malware-submission-pipeline compared with similar skills

All 4 of these similar skills score higher than building-automated-malware-submission-pipeline; compare them before choosing.

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building-automated-malware-submission-pipeline (this skill)by mukul9759633.3k25d agoSKILL.md
Agent-Reachby Panniantong10085.4k10d agoCLAUDE.md
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algorithmic-artby anthropics100177.9k3d agoSKILL.md
pptxby anthropics100177.9k3d agoSKILL.md

Frequently asked questions

How do I install building-automated-malware-submission-pipeline?
Run npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-automated-malware-submission-pipeline. The install tabs above show the steps for each supported agent.
Which AI agents does building-automated-malware-submission-pipeline work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is building-automated-malware-submission-pipeline safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is building-automated-malware-submission-pipeline still maintained?
The repository was last updated 25 days ago, so building-automated-malware-submission-pipeline is actively maintained.

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:

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):
        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_re

Truncated for display — read the full file on GitHub.

Related Skills

View on GitHub
GitHub Stars33.3k
CategorySecurity
Updated25d ago
Forks4.0k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions