backendaudit-python-library

v1.0.6 suspicious
6.0
Medium Risk

Local offline backend security, error handling, and code quality auditing tool for Python applications

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package presents a moderate risk due to its high obfuscation risk and lack of maintainer transparency, though it does not exhibit typical signs of a supply-chain attack.

  • High obfuscation risk due to eval() or exec() usage
  • Sparse maintainer information and missing GitHub repository
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access.
  • Shell: The use of shell=True can introduce security risks, especially with command injection vulnerabilities, but it may be necessary for certain functionalities.
  • Obfuscation: The presence of eval() or exec() on untrusted inputs suggests potential for code injection and obfuscation, indicating a higher risk.
  • Credentials: No patterns indicative of credential harvesting were detected, suggesting lower risk.
  • Metadata: The package lacks an associated GitHub repository and the maintainer's information is sparse, indicating potential unreliability.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_rules.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (7085 chars)
◈ Medium Contributing Guide 7.0

Some contribution signals present

  • Governance file: security.py
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 35 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • suggested_fix="Avoid using eval() or exec() on untrusted user inputs. Parse inputs using jso
⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • ts and set shell=False. E.g., subprocess.run(['ls', '-l'])" ))
  • # Check if shell=True is passed as keyword argument shell
  • and injection risk is high if shell=True and f-strings / string concatenation are used
  • ly built command string using shell=True.", file_path=fi
✓ 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

No GitHub repository linked

  • No GitHub repository link found
⚠ 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 backendaudit-python-library 
Create a comprehensive Python-based utility named 'BackendGuard' which leverages the 'backendaudit-python-library' to audit and enhance the security, error handling, and code quality of Python applications. This utility should be designed to run locally, making it accessible even when no internet connection is available. Here’s a detailed outline of the steps and features you should include:

1. **Setup and Installation**: Begin by setting up a virtual environment for your project and installing the 'backendaudit-python-library'. Ensure that the installation process is straightforward and documented.
2. **User Interface**: Design a simple command-line interface (CLI) that allows users to input the path to their Python application they wish to audit.
3. **Audit Process**: Implement functionalities within 'BackendGuard' to scan the provided Python application for common security vulnerabilities, error-prone code segments, and adherence to coding standards. Use the 'backendaudit-python-library' to perform these audits efficiently.
4. **Reporting**: After the audit process, generate a detailed report highlighting findings categorized into three sections: Security Issues, Error Handling Flaws, and Code Quality Recommendations. Each section should provide actionable insights and suggestions for improvement.
5. **Integration with IDEs**: As an optional advanced feature, explore integrating 'BackendGuard' with popular Python IDEs like PyCharm or VSCode, allowing real-time code analysis during development.
6. **Customization**: Allow users to customize the audit settings according to their specific needs, such as excluding certain directories from the audit or specifying particular coding standards.
7. **Documentation**: Finally, ensure that all aspects of 'BackendGuard', including setup, usage, and customization options, are well-documented. Include examples and best practices to guide users effectively.

By following these steps, 'BackendGuard' will become a valuable tool for developers looking to improve the robustness and reliability of their Python applications without relying on online services.

💬 Discussion Feed

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