# Performing Automated Malware Analysis With Cape

> 部署和操作 CAPEv2 沙箱，进行自动化恶意软件分析，具备行为监控、载荷提取、配置解析和反规避能力。

- Skill: `killvxk/performing-automated-malware-analysis-with-cape` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds@latest add killvxk/performing-automated-malware-analysis-with-cape`
- Raw SKILL.md: https://api.skillmd.com/api/skills/killvxk/performing-automated-malware-analysis-with-cape/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: Apache-2.0
- Author: killvxk (https://skillmd.com/u/killvxk)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/killvxk/performing-automated-malware-analysis-with-cape

---

# 使用 CAPE 进行自动化恶意软件分析

## 概述

CAPE（Config And Payload Extraction，配置与载荷提取）是一款从 Cuckoo 派生的开源恶意软件沙箱，可自动化执行行为分析、载荷转储和配置提取。CAPEv2 具备用于行为插桩的 API 钩挂功能，可捕获执行过程中创建/修改/删除的文件，以 PCAP 格式记录网络流量，并包含 70+ 个针对 Emotet、TrickBot、Cobalt Strike、AsyncRAT 和 Rhadamanthys 等家族的自定义配置提取器（cape-parsers）。签名系统包含 1000+ 个行为签名，可检测规避技术、持久化、凭据窃取和勒索软件行为。CAPE 的调试器支持通过在 YARA 签名中组合调试器操作来动态绕过反规避机制。推荐部署方式：Ubuntu LTS 宿主机 + Windows 10 21H2 客户机虚拟机。

## 前置条件

- Ubuntu 22.04 LTS 服务器（8+ CPU 核心、32GB+ 内存、500GB+ SSD）
- KVM/QEMU 虚拟化支持
- Windows 10 21H2 客户机镜像
- Python 3.9+，安装 CAPEv2 依赖项
- 用于隔离分析网络的网络配置

## 操作步骤

### 步骤 1：通过 API 提交和分析样本

```python
#!/usr/bin/env python3
"""用于自动化恶意软件提交和分析的 CAPE 沙箱 API 客户端。"""
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):
        """提交文件进行分析。"""
        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"[+] 已提交 {filepath} -> 任务 ID：{task_id}")
        return task_id

    def get_status(self, task_id):
        """检查任务分析状态。"""
        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):
        """等待分析完成。"""
        elapsed = 0
        while elapsed < max_wait:
            status = self.get_status(task_id)
            if status == "reported":
                print(f"[+] 任务 {task_id} 已完成")
                return True
            time.sleep(poll_interval)
            elapsed += poll_interval
            print(f"  等待中...（{elapsed}s，状态：{status}）")
        return False

    def get_report(self, task_id):
        """获取完整分析报告。"""
        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):
        """获取提取的恶意软件配置。"""
        report = self.get_report(task_id)
        configs = report.get("CAPE", {}).get("configs", [])
        return configs

    def get_dropped_files(self, task_id):
        """列出分析期间投放的文件。"""
        report = self.get_report(task_id)
        return report.get("dropped", [])

    def get_network_iocs(self, task_id):
        """从分析结果中提取网络 IoC。"""
        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):
        """完整的自动化分析流水线。"""
        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"用法：{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))
```

## 验证标准

- 样本在配置的超时时间内提交并完成分析
- 已知恶意软件家族触发了行为签名
- cape-parsers 成功提取恶意软件配置
- 网络流量已捕获并提取 IoC
- 已收集投放的文件和载荷以供进一步分析
- 反规避绕过对具有沙箱感知的恶意软件有效

## 参考资料

- [CAPEv2 GitHub](https://github.com/kevoreilly/CAPEv2)
- [CAPE 沙箱文档](https://capev2.readthedocs.io/)
- [使用 CAPE 自动化恶意软件分析](https://endsec.au/blog/building-an-automated-malware-sandbox-using-cape/)
- [在 Ubuntu 上安装 CAPEv2](https://medium.com/@rizqisetyokus/building-capev2-automated-malware-analysis-sandbox-part-1-da2a6ff69cdb)