ErisPulse-YunhuAdapter

v3.10.5 suspicious
4.0
Medium Risk

ErisPulse的云湖协议适配模块

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to network activity which could potentially be leveraged for unauthorized data transfer, despite showing low risks in other areas such as shell execution and obfuscation.

  • Moderate network risk
  • Low maintenance and metadata quality
Per-check LLM notes
  • Network: The package establishes network sessions that could be used for legitimate communication but may also indicate potential for unauthorized data transfer.
  • Shell: No shell execution patterns were detected, indicating low risk of direct command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low maintenance and metadata quality, but there are no clear indications of malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • self._adapter.session = aiohttp.ClientSession() headers = {} if "jwznb.co
  • n: self.session = aiohttp.ClientSession() json_data = json.dumps(data) if data else None
  • n: self.session = aiohttp.ClientSession() headers = {"Content-Type": "text/plain"} t
  • self._ws_sessions[bot_name] = aiohttp.ClientSession() retry_interval = 5 while self._is_runnin
  • n: self.session = aiohttp.ClientSession() self._is_running = True enabled_bots = {n
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

⚠ Registered Email Domain score 3.0

Suspicious email domain flags: Very short email domain: qq.com>

  • Very short email domain: qq.com>
✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ErisPulse/ErisPulse-YunhuAdapter appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ErisPulse-YunhuAdapter 
构建一个名为 'CloudLakeMonitor' 的小型应用，该应用将利用 'ErisPulse-YunhuAdapter' 包来监控和分析云湖协议下的环境数据。此应用旨在为用户提供实时的数据展示，并支持数据分析与报警功能。

### 应用核心功能
1. **实时数据获取**：通过'ErisPulse-YunhuAdapter'从云湖协议中读取实时的环境数据（如温度、湿度等）。
2. **数据可视化**：使用图表展示数据的变化趋势，如折线图或柱状图。
3. **数据分析**：提供基本的数据统计功能，例如平均值、最大值和最小值等。
4. **报警机制**：当检测到异常数据时（例如温度超出预设范围），系统应自动发送警报通知给用户。
5. **配置管理**：允许用户自定义报警阈值和其他参数设置。

### 开发步骤
1. **环境搭建**：确保Python环境已安装必要的库，包括'ErisPulse-YunhuAdapter'。
2. **数据获取**：编写代码调用'ErisPulse-YunhuAdapter'接口，获取环境数据。
3. **界面设计**：设计友好的用户界面，用于显示数据和进行配置。
4. **实现功能**：根据上述核心功能要求，逐步实现各项功能。
5. **测试优化**：进行全面的功能测试，确保所有功能正常工作并进行必要的性能优化。
6. **部署上线**：将应用部署到服务器上，供用户访问。

### 如何使用'ErisPulse-YunhuAdapter'
- 初始化连接至云湖协议的数据源。
- 定时或实时获取数据流。
- 处理接收到的数据，提取关键信息。
- 利用处理后的数据更新前端展示及触发报警条件。

通过以上步骤，你可以创建一个强大而实用的应用程序，帮助用户更好地理解和管理他们的环境数据。