AI Analysis
Final verdict: SUSPICIOUS
The package shows no immediate signs of malicious intent based on current checks, but its recent creation and lack of description raise some concerns about potential future risks.
- Metadata risk due to recent creation
- Lack of detailed package description
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate risk of command injection or system compromise.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package appears to be newly created with limited activity, raising some suspicion but not definitive evidence of malice.
Package Quality Overall: Low (1.2/10)
○ 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
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ 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
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
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 4.0
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "secemp9" 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 atomic_ast
Create a Python-based code analysis tool named 'CodeSnoop' using the 'atomic_ast' package. This tool should serve as a basic static code analyzer capable of parsing Python source code into an abstract syntax tree (AST), analyzing it for common issues like unused imports, redundant code blocks, and potential security vulnerabilities. Here's a detailed breakdown of the project requirements: 1. **Project Setup**: Initialize a new Python environment and install the 'atomic_ast' package. If 'atomic_ast' is not available in PyPI, assume it's a custom package provided by the user. 2. **Code Parsing**: Use 'atomic_ast' to parse input Python files into ASTs. Ensure the tool can handle multiple file inputs at once. 3. **Analysis Features**: - **Unused Imports**: Identify and list all import statements that are not used within the code. - **Redundant Code**: Detect and highlight sections of code that perform unnecessary operations. - **Security Checks**: Implement simple checks for common security issues such as hard-coded passwords or unsafe HTTP requests. 4. **User Interface**: Develop a command-line interface (CLI) for interacting with 'CodeSnoop'. Users should be able to specify files or directories to analyze and receive a report of findings. 5. **Output Reporting**: Generate a readable report detailing the analysis results. The report should categorize findings into warnings, errors, and informational messages. 6. **Testing**: Write unit tests to ensure 'CodeSnoop' works correctly on various types of Python code. Include test cases for edge scenarios and complex code structures. 7. **Documentation**: Provide comprehensive documentation explaining how to use 'CodeSnoop', including setup instructions, usage examples, and a guide for developers contributing to the project. By the end of this project, you will have a functional static code analysis tool that leverages the 'atomic_ast' package to enhance Python code quality and security.
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