apmtools

v0.1.22 suspicious
5.0
Medium Risk

A collection of tools for processing air pollution monitoring data

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to the use of eval with dynamically constructed strings, which can be a vector for code injection. While there's no concrete evidence of malicious intent, the incomplete metadata and potential for hidden activities warrant caution.

  • High obfuscation risk due to eval usage
  • Incomplete author and maintainer information
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating no unexpected system command executions.
  • Obfuscation: The use of eval with dynamically constructed strings suggests obfuscation or potential code injection risks.
  • Credentials: No direct evidence of credential harvesting is present, but the unusual use of eval could indicate attempts to hide such activities.
  • Metadata: The author information is incomplete and the maintainer seems new or inactive, which raises some suspicion but not enough to conclude malice.

📦 Package Quality Overall: Low (3.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 8 type-annotated function signatures (partial)
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in federlorenz/apmtools
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • if not eval("value.__getattr__(\""+i+"\")" + j):
  • if not eval("value.__getattr__('m')[\""+i+"\"]" + j):
  • if eval("value.__getattr__('m')[\""+i+"\"]" + j):
  • if eval("value.__getattr__(\""+i+"\")" + j):
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository federlorenz/apmtools appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with apmtools 
Create a Python-based mini-application called 'AirQualityMonitor' that leverages the 'apmtools' package to process and analyze air pollution monitoring data. This application will serve as a tool for environmental scientists and policymakers to better understand local air quality conditions. Here’s a detailed breakdown of what the application should include:

1. **Data Import**: Develop functionality to import air pollution data from CSV files or directly from a database. Use 'apmtools' to handle the initial data cleaning and normalization.
2. **Real-Time Data Streaming**: Implement a feature to stream live air quality data from a simulated API endpoint. Utilize 'apmtools' to preprocess the incoming data in real-time.
3. **Data Analysis**: Incorporate advanced analysis using 'apmtools', including calculating pollutant concentrations over time, identifying trends, and detecting anomalies.
4. **Visualization**: Create visual representations of the analyzed data using matplotlib or seaborn. Show hourly, daily, and monthly trends of pollutants like PM2.5, NO2, etc.
5. **Reporting**: Generate comprehensive reports based on the analysis, including summary statistics, trend charts, and anomaly detection insights.
6. **User Interface**: Design a simple web interface using Flask or Django to display the real-time data, historical trends, and report summaries.
7. **Alert System**: Integrate an alert system that notifies users via email or SMS when pollutant levels exceed certain thresholds.
8. **Documentation**: Provide clear documentation explaining how to install the application, run it, and interpret the results.

Ensure that 'apmtools' is utilized throughout the application to streamline data processing tasks, making the development process more efficient and the final product more robust.

💬 Discussion Feed

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