# Analyzing Supply Chain Malware Artifacts

> 调查供应链攻击工件，包括被木马化的软件更新、被攻陷的构建流水线和侧载的依赖项，以识别入侵向量和攻陷范围。

- Skill: `killvxk/analyzing-supply-chain-malware-artifacts` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds@latest add killvxk/analyzing-supply-chain-malware-artifacts`
- Raw SKILL.md: https://api.skillmd.com/api/skills/killvxk/analyzing-supply-chain-malware-artifacts/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/analyzing-supply-chain-malware-artifacts

---

# 分析供应链恶意软件工件

## 概述

供应链攻击通过破坏合法软件分发渠道，借助受信任的更新机制投递恶意软件。典型案例包括 SolarWinds SUNBURST（2020 年，影响 18,000 多个客户）、3CX SmoothOperator（2023 年，一起源自 Trading Technologies 的级联供应链攻击）以及大量 npm/PyPI 包投毒活动。分析工作涉及：将木马化二进制文件与合法版本进行比对、识别构建工件中的注入代码、检查代码签名异常，以及追踪从初始攻陷到载荷投递的感染链。截至 2025 年，供应链攻击占所有违规事件的 30%，较前几年增加了 100%。

## 前置条件

- Python 3.9+，安装 `pefile`、`ssdeep`、`hashlib`
- 二进制对比工具（BinDiff、Diaphora）
- 代码签名验证工具（sigcheck、codesign）
- 软件成分分析（SCA）工具
- 访问合法软件版本以供比对
- 包仓库监控（npm、PyPI、NuGet）

## 操作步骤

### 步骤 1：二进制比对分析

```python
#!/usr/bin/env python3
"""比对木马化二进制文件与合法版本。"""
import hashlib
import pefile
import sys
import json


def compare_pe_files(legitimate_path, suspect_path):
    """比对合法版本与可疑版本之间的 PE 文件结构。"""
    legit_pe = pefile.PE(legitimate_path)
    suspect_pe = pefile.PE(suspect_path)

    report = {"differences": [], "suspicious_sections": [], "import_changes": []}

    # 比对节
    legit_sections = {s.Name.rstrip(b'\x00').decode(): {
        "size": s.SizeOfRawData,
        "entropy": s.get_entropy(),
        "characteristics": s.Characteristics,
    } for s in legit_pe.sections}

    suspect_sections = {s.Name.rstrip(b'\x00').decode(): {
        "size": s.SizeOfRawData,
        "entropy": s.get_entropy(),
        "characteristics": s.Characteristics,
    } for s in suspect_pe.sections}

    # 查找新增或已修改的节
    for name, props in suspect_sections.items():
        if name not in legit_sections:
            report["suspicious_sections"].append({
                "name": name, "reason": "合法版本中不存在的新节",
                "size": props["size"], "entropy": round(props["entropy"], 2),
            })
        elif abs(props["size"] - legit_sections[name]["size"]) > 1024:
            report["suspicious_sections"].append({
                "name": name, "reason": "节大小发生显著变化",
                "legit_size": legit_sections[name]["size"],
                "suspect_size": props["size"],
            })

    # 比对导入
    legit_imports = set()
    if hasattr(legit_pe, 'DIRECTORY_ENTRY_IMPORT'):
        for entry in legit_pe.DIRECTORY_ENTRY_IMPORT:
            for imp in entry.imports:
                if imp.name:
                    legit_imports.add(f"{entry.dll.decode()}!{imp.name.decode()}")

    suspect_imports = set()
    if hasattr(suspect_pe, 'DIRECTORY_ENTRY_IMPORT'):
        for entry in suspect_pe.DIRECTORY_ENTRY_IMPORT:
            for imp in entry.imports:
                if imp.name:
                    suspect_imports.add(f"{entry.dll.decode()}!{imp.name.decode()}")

    new_imports = suspect_imports - legit_imports
    if new_imports:
        report["import_changes"] = list(new_imports)

    # 检查代码签名
    report["legit_signed"] = bool(legit_pe.OPTIONAL_HEADER.DATA_DIRECTORY[4].Size)
    report["suspect_signed"] = bool(suspect_pe.OPTIONAL_HEADER.DATA_DIRECTORY[4].Size)

    return report


def hash_file(filepath):
    """计算文件的多种哈希值。"""
    hashes = {}
    with open(filepath, 'rb') as f:
        data = f.read()
    for algo in ['md5', 'sha1', 'sha256']:
        h = hashlib.new(algo)
        h.update(data)
        hashes[algo] = h.hexdigest()
    return hashes


if __name__ == "__main__":
    if len(sys.argv) < 3:
        print(f"用法：{sys.argv[0]} <legitimate_binary> <suspect_binary>")
        sys.exit(1)
    report = compare_pe_files(sys.argv[1], sys.argv[2])
    print(json.dumps(report, indent=2, ensure_ascii=False))
```

## 验证标准

- 通过二进制对比识别出被木马化的组件
- 注入的代码被隔离并单独分析
- 代码签名异常已记录
- 从构建工件重建感染时间线
- 评估对受影响系统的下游影响范围
- 提取 IoC 用于检测和封锁

## 参考资料

- [ReversingLabs - 3CX 供应链分析](https://www.reversinglabs.com/blog/what-went-wrong-with-the-3cx-software-supply-chain-attack-and-how-it-could-have-been-prevented)
- [Fortinet - SolarWinds 供应链攻击](https://www.fortinet.com/resources/cyberglossary/solarwinds-cyber-attack)
- [Picus - 3CX SmoothOperator 分析](https://www.picussecurity.com/resource/blog/smoothoperator-analysis-of-3cxdesktopapp-supply-chain-attack)
- [MITRE ATT&CK T1195 - 供应链攻陷](https://attack.mitre.org/techniques/T1195/)