api-graveyard

v0.1.2 suspicious
4.0
Medium Risk

Official Python collector for API Graveyard — automatically tracks your outgoing HTTP dependencies

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate network risk due to its interaction with external services, while other risks like shell execution, obfuscation, and credential harvesting are minimal. However, the metadata risk is elevated due to low activity and lack of detailed information from the maintainer.

  • Moderate network risk
  • Elevated metadata risk
Per-check LLM notes
  • Network: The observed network call patterns may be legitimate if the package is designed to interact with external APIs, but they could also indicate potential data exfiltration.
  • Shell: No shell execution patterns detected, which suggests low risk for direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: Suspicious low activity and lack of details from the maintainer increase risk of potential malice.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

  • Test runner config found: pyproject.toml
  • 2 test file(s) detected (e.g. test_batcher.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2498 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 6 type-annotated function signatures (partial)
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 3 commits in Shakargy/api-graveyard-python
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • de("utf-8") req = urllib.request.Request( self._endpoint, dat
  • ) with urllib.request.urlopen(req, timeout=10) as resp: if self._d
✓ 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Single contributor with only 3 commit(s) — possibly throwaway account
⚠ 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 api-graveyard 
Develop a comprehensive monitoring tool named 'API Guardian' using the Python package 'api-graveyard'. This tool will serve as an essential utility for developers to track and manage their outgoing HTTP requests efficiently. The main objective of 'API Guardian' is to provide real-time insights into which APIs are being accessed, their frequency, and any potential issues that arise from these interactions.

Step-by-Step Development Guide:
1. **Setup Environment**: Begin by setting up a Python virtual environment and installing the necessary packages including 'api-graveyard'.
2. **Integrate 'api-graveyard'**: Utilize the 'api-graveyard' package to monitor outgoing HTTP requests from your application. Ensure it captures the URL, method type (GET, POST, etc.), headers, and response status codes.
3. **Database Integration**: Implement a database (such as SQLite or PostgreSQL) to store the collected data for historical analysis and reporting purposes.
4. **Real-Time Alerts**: Configure the tool to send real-time alerts via email or SMS when certain conditions are met, such as encountering frequent errors or unusual usage patterns.
5. **User Interface**: Develop a simple yet effective web interface using Flask or Django to display the collected data in an easily digestible format. Include features like charts for visual representation and filters to refine search results.
6. **Security Measures**: Incorporate security best practices, ensuring sensitive information is handled securely and access to the tool is restricted based on user roles.
7. **Testing & Documentation**: Conduct thorough testing to ensure all functionalities work as expected and create comprehensive documentation detailing setup instructions, usage guidelines, and troubleshooting tips.

Suggested Features:
- Detailed logs of each HTTP request and response.
- Customizable alert rules based on specific criteria.
- Historical trend analysis and anomaly detection.
- User authentication and role-based access control.
- Export options for data analysis outside of the tool.

How 'api-graveyard' Package is Utilized:
- The 'api-graveyard' package will be leveraged to intercept and log outgoing HTTP requests made by the application. It provides hooks and decorators that can be integrated seamlessly into existing codebases to gather necessary metadata about these requests without altering the application's core functionality. This data will then be processed and stored according to the defined schema in the chosen database.

💬 Discussion Feed

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