mirror of https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git synced 2026-08-04 18:00:18 +03:00

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T

Mahipal 2fb6a9faff Rewrite 548 skill descriptions to the activation rubric

Each rewritten description now states both what the skill does (concrete
capability, named tools/artifacts) and an explicit when-to-use trigger,
improving agent discovery/activation. Grounded in each skill's own body;
changes confined to the `description` field only (bodies and all other
frontmatter untouched). Produced by a gated audit->rewrite->recheck loop
(548 -> 0 flagged) with a sampled anti-invention check (0 ungrounded).

Schema: 817/817 pass. Framework-ID gate: 0 defects.

2026-08-02 09:32:13 -07:00

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Performing Automated Malware Analysis with CAPE

Overview

CAPE (Config And Payload Extraction) is an open-source malware sandbox derived from Cuckoo that automates behavioral analysis, payload dumping, and configuration extraction. CAPEv2 features API hooking for behavioral instrumentation, captures files created/modified/deleted during execution, records network traffic in PCAP format, and includes 70+ custom configuration extractors (cape-parsers) for families like Emotet, TrickBot, Cobalt Strike, AsyncRAT, and Rhadamanthys. The signature system includes 1000+ behavioral signatures detecting evasion techniques, persistence, credential theft, and ransomware behavior. CAPE's debugger enables dynamic anti-evasion bypasses combining debugger actions within YARA signatures. Recommended deployment: Ubuntu LTS host with Windows 10 21H2 guest VM.

When to Use

When conducting security assessments that involve performing automated malware analysis with cape
When following incident response procedures for related security events
When performing scheduled security testing or auditing activities
When validating security controls through hands-on testing

Prerequisites

Ubuntu 22.04 LTS server (8+ CPU cores, 32GB+ RAM, 500GB+ SSD)
KVM/QEMU virtualization support
Windows 10 21H2 guest image
Python 3.9+ with CAPEv2 dependencies
Network configuration for isolated analysis network

Workflow

Step 1: Submit and Analyze Samples via API

#!/usr/bin/env python3
"""CAPE sandbox API client for automated malware submission and analysis."""
import requests
import json
import time
import sys
from pathlib import Path


class CAPEClient:
    def __init__(self, base_url="http://localhost:8000", api_token=None):
        self.base_url = base_url.rstrip("/")
        self.headers = {}
        if api_token:
            self.headers["Authorization"] = f"Token {api_token}"

    def submit_file(self, filepath, options=None):
        """Submit a file for analysis."""
        url = f"{self.base_url}/apiv2/tasks/create/file/"
        files = {"file": open(filepath, "rb")}
        data = options or {}
        data.setdefault("timeout", 120)
        data.setdefault("enforce_timeout", False)

        resp = requests.post(url, files=files, data=data, headers=self.headers)
        resp.raise_for_status()
        result = resp.json()
        task_id = result.get("data", {}).get("task_ids", [None])[0]
        print(f"[+] Submitted {filepath} -> Task ID: {task_id}")
        return task_id

    def get_status(self, task_id):
        """Check task analysis status."""
        url = f"{self.base_url}/apiv2/tasks/status/{task_id}/"
        resp = requests.get(url, headers=self.headers)
        return resp.json().get("data", "unknown")

    def wait_for_completion(self, task_id, poll_interval=15, max_wait=600):
        """Wait for analysis to complete."""
        elapsed = 0
        while elapsed < max_wait:
            status = self.get_status(task_id)
            if status == "reported":
                print(f"[+] Task {task_id} completed")
                return True
            time.sleep(poll_interval)
            elapsed += poll_interval
            print(f"  Waiting... ({elapsed}s, status: {status})")
        return False

    def get_report(self, task_id):
        """Retrieve full analysis report."""
        url = f"{self.base_url}/apiv2/tasks/get/report/{task_id}/"
        resp = requests.get(url, headers=self.headers)
        return resp.json()

    def get_config(self, task_id):
        """Get extracted malware configuration."""
        report = self.get_report(task_id)
        configs = report.get("CAPE", {}).get("configs", [])
        return configs

    def get_dropped_files(self, task_id):
        """List files dropped during analysis."""
        report = self.get_report(task_id)
        return report.get("dropped", [])

    def get_network_iocs(self, task_id):
        """Extract network IOCs from analysis."""
        report = self.get_report(task_id)
        network = report.get("network", {})
        iocs = {
            "dns": [d.get("request") for d in network.get("dns", [])],
            "http": [h.get("uri") for h in network.get("http", [])],
            "tcp": [f"{h.get('dst')}:{h.get('dport')}"
                    for h in network.get("tcp", [])],
        }
        return iocs

    def analyze_sample(self, filepath):
        """Full automated analysis pipeline."""
        task_id = self.submit_file(filepath)
        if not task_id:
            return None

        if self.wait_for_completion(task_id):
            report = {
                "task_id": task_id,
                "config": self.get_config(task_id),
                "network_iocs": self.get_network_iocs(task_id),
                "dropped_files": len(self.get_dropped_files(task_id)),
            }
            return report
        return None


if __name__ == "__main__":
    if len(sys.argv) < 2:
        print(f"Usage: {sys.argv[0]} <malware_sample> [cape_url]")
        sys.exit(1)

    url = sys.argv[2] if len(sys.argv) > 2 else "http://localhost:8000"
    client = CAPEClient(url)
    result = client.analyze_sample(sys.argv[1])
    if result:
        print(json.dumps(result, indent=2))

Validation Criteria

Samples submitted and analyzed within configured timeout
Behavioral signatures triggered for known malware families
Malware configurations extracted by cape-parsers
Network traffic captured and IOCs extracted
Dropped files and payloads collected for further analysis
Anti-evasion bypasses effective against sandbox-aware malware

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