Performing Automated Malware Analysis With Cape

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

Detection Gaps & Validation

• Sandbox-aware malware stalls or branches to benign behavior. CAPE samples detect KVM/QEMU via CPUID hypervisor bit, the vioscsi/balloon drivers, MAC OUI 08:00:27, low core/RAM counts, and SbieDll.dll. A "clean" report often means evasion, not a benign file — check the behavior signatures for anti-VM/anti-sandbox hits before clearing a sample.
• Long sleeps and time-bombs beat the default timeout. Malware calls NtDelayExecution/Sleep past the 120s window or fires only on a specific date/locale. Enable CAPE's sleep-skip/time-travel, raise enforce_timeout, and confirm the process actually reached its payload stage rather than just exiting.
• Geofencing and dead C2 produce empty network sections. If the sample checks GetUserDefaultLangID or resolves C2 by region, route through an exit in the target geography and re-run; absence of C2 traffic is not proof of benignity.
• No-detonation = no config. cape-parsers only extract config when the unpacking stub runs. If CAPE.configs is empty but the sample is packed, the payload never executed — check for a guardrail (missing arg, mutex, parent-process check) and re-submit with the right trigger.
• Benign-lookalike FPs: installers (NSIS/InnoSetup), packed-but-legitimate software, and admin tools (PsExec, AnyDesk) trip injection/persistence signatures. Confirm via dropped-file hashes on VT and cross-tool static review before labeling malicious.

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

References

• CAPEv2 GitHub
• CAPE Sandbox Documentation
• Automating Malware Analysis with CAPE
• Installing CAPEv2 on Ubuntu