ai-slop-detector

v3.8.2 suspicious
4.0
Medium Risk

Evidence-based static analyzer for detecting AI-generated code quality issues with context-aware validation

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has some suspicious elements such as git commands that could be used for unintended purposes, and missing repository metadata which raises concerns about the package's origin and maintenance.

  • Shell risk due to use of git commands
  • Missing repository metadata
Per-check LLM notes
  • Network: No network calls detected, minimal risk.
  • Shell: Git commands suggest the package may be gathering version control information, potentially for logging or identifying purposes.
  • Obfuscation: The base64 decoding of saml_response appears to be for parsing XML data, likely not malicious.
  • Credentials: No patterns indicative of credential harvesting detected.
  • Metadata: The repository is not found, and the author's information is sparse, indicating potential risks.

📦 Package Quality Overall: Medium (5.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://ai-slop-detector.readthedocs.io
  • Detailed PyPI description (36465 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 419 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • se decoded_response = base64.b64decode(saml_response) # Parse XML (simplified - production
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • commit = ( subprocess.run( ["git", "rev-parse", "--short", "HEAD"],
  • branch = ( subprocess.run( ["git", "branch", "--show-current"],
  • y""" try: subprocess.run( ["git", "rev-parse", "--git-dir"], cwd=path
  • h commit_result = subprocess.run( ["git", "rev-parse", "HEAD"], cwd=path, cap
  • h branch_result = subprocess.run( ["git", "rev-parse", "--abbrev-ref", "HEAD"
  • try: result = subprocess.run( ["git", "diff", "--cached", "--name-only",
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: flamehaven.space>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ 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 ai-slop-detector 
Create a Python-based code analysis tool named 'CodeQualityGuard' that leverages the 'ai-slop-detector' package to analyze and improve the quality of Python code. This tool will serve as an essential utility for developers looking to ensure their code adheres to high-quality standards and isn't influenced by AI-generated slop. Here’s a detailed plan on how to develop this application:

1. **Project Setup**: Begin by setting up a new Python environment and installing the necessary packages, including 'ai-slop-detector'. Ensure that you also install any other dependencies required for your project.
2. **User Interface Design**: Develop a simple yet effective command-line interface (CLI) where users can input paths to their Python files or directories containing multiple Python files for analysis.
3. **Code Analysis Functionality**: Utilize 'ai-slop-detector' to perform static analysis on the provided code. This involves parsing the code, identifying potential issues related to AI-generated slop, such as redundant imports, unnecessary complexity, poor variable naming conventions, etc., and providing detailed reports on these findings.
4. **Context-Aware Validation**: Implement context-aware validation within your tool. This means that the analysis should take into account the specific context in which certain coding practices are used. For example, a piece of code might be considered good practice in one scenario but bad practice in another. Your tool should be able to differentiate between these contexts and provide accurate feedback.
5. **Reporting Mechanism**: Design a reporting mechanism that presents the findings from the analysis in a user-friendly format. This could include a summary of all detected issues, suggestions for improvement, and a score indicating the overall quality of the code.
6. **Integration with Version Control Systems**: Optionally, integrate your tool with popular version control systems like Git. This would allow users to analyze code changes before committing them, ensuring that only high-quality code is integrated into the repository.
7. **Continuous Integration/Continuous Deployment (CI/CD) Integration**: Explore integrating your tool into CI/CD pipelines so that it automatically runs every time code is pushed to a repository, providing real-time feedback on code quality.
8. **Documentation and User Support**: Provide comprehensive documentation for both developers and end-users. Include examples, tutorials, and FAQs to help users understand how to use the tool effectively.

Throughout the development process, focus on making 'CodeQualityGuard' not just a tool for identifying problems but also a resource for learning and improving coding skills. Use 'ai-slop-detector'’s capabilities to their fullest extent, ensuring that your tool is robust, reliable, and highly useful for any developer looking to maintain high standards in their code.