aicheck

v0.1.8 suspicious
6.0
Medium Risk

Catch AI-generated code issues before they catch you

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to potential obfuscation and unusual metadata, but lacks clear indicators of malicious intent such as network or shell execution risks.

  • High obfuscation risk due to dynamic import and attribute access.
  • Suspicious git repository activity and maintainer history.
Per-check LLM notes
  • Network: The network call appears to be checking for package information from PyPI, which is generally benign.
  • Shell: No shell execution patterns detected, indicating no immediate risk from this aspect.
  • Obfuscation: The code pattern suggests dynamic import and attribute access which could be used for obfuscation to hide the actual functionality of the module.
  • Credentials: No direct evidence of credential harvesting is found in the provided snippet.
  • Metadata: High risk due to suspicious git repository activity and maintainer history.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

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

Some documentation present

  • Detailed PyPI description (1962 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 31 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 1 commits in maheshmakvana/ai-check
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • pass try: resp = requests.get( f"https://pypi.org/pypi/{top}/json",
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • try: mod = __import__(mod_name) for attr in dir(mod): k
✓ 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: users.noreply.github.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 7.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Very few commits: 1 total
  • Single contributor with only 1 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 aicheck 
Create a Python-based desktop application named 'CodeGuard' that helps developers detect potential issues in their code generated by AI tools. The application will leverage the 'aicheck' package to analyze Python scripts and identify common pitfalls such as security vulnerabilities, logical errors, and adherence to coding standards. Here’s a step-by-step guide on how to build this application:

1. **Project Setup**: Initialize your Python environment with necessary packages including 'aicheck', 'tkinter' for the GUI, and 'pycodestyle' for additional linting.
2. **User Interface Design**: Design a simple and intuitive user interface using Tkinter where users can upload Python files or paste code directly into a text box.
3. **Code Analysis Functionality**: Implement a function that takes the input code from the UI, passes it through the 'aicheck' package to scan for issues, and also runs pycodestyle for basic linting checks.
4. **Result Display**: Once the analysis is complete, display the results back to the user in a readable format within the GUI. Highlight any critical issues found and provide suggestions for fixing them.
5. **Advanced Features** (Optional): Consider adding features like saving the analysis report, exporting results to PDF, or integrating with version control systems for continuous integration.
6. **Testing and Validation**: Thoroughly test the application with various types of Python code to ensure it accurately identifies issues and provides useful feedback.
7. **Documentation**: Create comprehensive documentation detailing how to use the application, how to integrate it into existing workflows, and any known limitations.

This project not only leverages the 'aicheck' package effectively but also enhances developer productivity by ensuring code quality and security.