building-automated-malware-submission-pipeline

building-automated-malware-submission-pipeline is a skill for Claude Code, Codex from adriannoes/awesome-agentic-ai. It costs 72 tokens per session (4,337 once invoked), scanned A, original, MIT.

A guide for automating the collection and analysis of suspicious files from endpoints and email systems. It submits files to isolated malware sandboxes and scanning services, then produces verdicts and indicators of compromise for security monitoring.

In plain words
What is it for?
Use it to connect endpoint or email quarantine sources with sandbox tools, malware databases, and multi-engine scanners. It helps identify malware families and send extracted indicators to a SIEM, a system that collects and analyzes security logs.
Why use it?
Manually submitting every suspicious file can slow down a security operations team. Automation helps handle larger alert volumes and return analysis results more quickly.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to connect endpoint or email quarantine sources with sandbox tools, malware databases, and multi-engine scanners. It helps identify malware families and send extracted indicators to a SIEM, a system that collects and analyzes security logs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/adriannoes/awesome-agentic-ai/building-automated-malware-submission-pipeline
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add adriannoes/awesome-agentic-ai --skill building-automated-malware-submission-pipeline
Clone the repo
git clone --depth 1 https://github.com/adriannoes/awesome-agentic-ai

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for building-automated-malware-submission-pipeline

README.md
[![agentmods](https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/building-automated-malware-submission-pipeline/github.svg)](https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/building-automated-malware-submission-pipeline)
Your own site
<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/building-automated-malware-submission-pipeline"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/building-automated-malware-submission-pipeline/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for building-automated-malware-submission-pipeline

Your own site · 80×15
<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/building-automated-malware-submission-pipeline"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/building-automated-malware-submission-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,337 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 3 findings, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Data Exfiltration · line 387
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 401
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 406
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00072 $0.04337
Opus 5 $0.00036 $0.02168
Sonnet 5 $0.00014 $0.00867
Haiku 4.5 $0.00007 $0.00434

Measured 12d ago against content hash 752bc5ccf0d1, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

building-automated-malware-submission-pipeline scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/agent.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

response = requests.get(
Origin

Copies of this mod

3 near-identical copies found in the catalogue:

cursor-claude-codex/skills/anthropic-cybersecurity-skills/skills/building-automated-malware-submission-pipeline/SKILL.md · 508 lines

How it starts

The opening of the file, as written. The whole thing — 508 lines — stays where its author put it; the contents beside it link to each section on 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, 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)
        }

Read the full file on GitHub · 508 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 508 lines · 72 tokens per session scan A 752bc5ccf0d1

Subscribe to this mod's changes

building-automated-malware-submission-pipeline is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 14d ago), licensed MIT. It adds 72 tokens to every session and 4,337 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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