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.
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
Practical Steps
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
Compliance Framework Mapping
This skill supports compliance evidence collection across multiple frameworks:
- SOC 2: CC7.2 (Anomaly Detection), CC7.4 (Incident Response)
- ISO 27001: A.12.2 (Malware Protection), A.16.1 (Security Incident Management)
- NIST 800-53: SI-3 (Malicious Code Protection), IR-4 (Incident Handling)
- NIST CSF: DE.CM (Continuous Monitoring), RS.AN (Analysis)
Claw GRC Tip: When this skill is executed by a registered agent, compliance evidence is automatically captured and mapped to the relevant controls in your active frameworks.
Deploying This Skill with Claw GRC
Agent Execution
Register this skill with your Claw GRC agent for automated execution:
# Install via CLI
npx claw-grc skills add performing-automated-malware-analysis-with-cape
# Or load dynamically via MCP
grc.load_skill("performing-automated-malware-analysis-with-cape")Audit Trail Integration
When executed through Claw GRC, every step of this skill generates tamper-evident audit records:
- SHA-256 chain hashing ensures no step can be modified after execution
- Evidence artifacts (configs, scan results, logs) are automatically attached to relevant controls
- Trust score impact — successful execution increases your agent's trust score
Continuous Compliance
Schedule this skill for recurring execution to maintain continuous compliance posture. Claw GRC monitors for drift and alerts when re-execution is needed.