aicheat

v0.7.0 suspicious
7.0
High Risk

A command line AI coding assistant

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package lacks a clear maintainer and associated repository, raising concerns about its origin and reliability. This could indicate potential risks such as a supply-chain attack.

  • Sparse maintainer information
  • No associated GitHub repository
Per-check LLM notes
  • Metadata: The package has no associated GitHub repository and the maintainer's information is sparse, indicating potential unreliability.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • Test runner config found: conftest.py
  • 5 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (9765 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 5.0

Partial type annotation coverage

  • 216 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 score 6.0

Found 4 network call pattern(s)

  • {url=}") response = httpx.get(url) response.raise_for_status() try:
  • t policy } response = httpx.get( api_base, params={ "action": "o
  • return None response = httpx.get( api_base, params={ "action": "q
  • ) response = httpx.get( url, params=url.params,
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • _info >= (3, 13): exec(compiled_code, globals=globals_, locals=globals_) else:
  • s_) else: exec(compiled_code, globals_, globals_) except Exception as exc:
  • try: compiled_code = compile(code, "aicheat_execute_python_code.py", mode="exec") if sys.version_info >= (3, 13): exec
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • ommand}"): proc = subprocess.Popen( [ shell_path,
  • nt) # dry run proc = subprocess.Popen( ["patch", "-p1", "--dry-run", str(path)], s
  • patch_io.seek(0) proc = subprocess.Popen( ["patch", "-p1", "--force", str(path)], std
  • ly try: result = subprocess.run( ["man", arg], capture_output=True,
  • s) try: output = subprocess.check_output(cmd, text=True) except subprocess.CalledProcessError as
  • ELL", "/bin/bash") proc = subprocess.Popen( [ shell_path, "-il", # for
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: distruzione.org>

✓ 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 aicheat 
Create a fully-functional mini-app named 'CodeAssist' using the Python package 'aicheat'. CodeAssist should serve as an interactive command-line interface where developers can seek assistance for common coding problems. The app should be able to provide solutions, code snippets, and explanations for issues ranging from syntax errors to more complex logic errors.

### Core Features:
1. **Syntax Help**: Users can input a piece of code that contains a syntax error, and CodeAssist will identify the error and suggest corrections.
2. **Logic Assistance**: For logical errors, users can describe their problem or input problematic code, and CodeAssist will analyze the logic and offer potential fixes or alternative approaches.
3. **Code Snippets**: Provide ready-to-use code snippets for common programming tasks based on user queries or selected categories.
4. **Interactive Mode**: Allow users to engage in an interactive dialogue where they can ask follow-up questions about the provided solutions or seek further clarification.
5. **Learning Resources**: Recommend relevant tutorials or documentation based on the user's query or the type of help requested.

### How to Utilize 'aicheat':
- Use 'aicheat' to integrate AI-driven analysis and response capabilities into CodeAssist. This includes leveraging its natural language processing (NLP) to understand user inputs, whether they are described problems or actual code snippets.
- Implement 'aicheat' to generate responses that not only fix immediate issues but also explain the reasoning behind the solution, helping users learn from the process.
- Incorporate 'aicheat' functionalities to continuously improve the quality of responses through machine learning models trained on various coding scenarios.

### Development Steps:
1. Set up a virtual environment and install necessary packages including 'aicheat'.
2. Design the CLI interface for CodeAssit, ensuring it is user-friendly and intuitive.
3. Develop the backend logic that integrates 'aicheat' for analyzing and responding to user inputs.
4. Implement features for syntax help, logic assistance, code snippets, and interactive mode as outlined above.
5. Test the application thoroughly with different types of user inputs and scenarios to ensure reliability and accuracy.
6. Enhance the application with additional features such as integration with version control systems for context-aware assistance.
7. Document the project and create usage instructions for other developers.