argot-engine

v0.2.39 suspicious
6.0
Medium Risk

(No description)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has significant obfuscation risks due to the use of eval(), which could lead to code injection vulnerabilities. While there's no concrete evidence of malicious activity, the low maintainer effort and lack of transparency add to the suspicion.

  • High obfuscation risk due to eval()
  • Signs of low maintainer effort
Per-check LLM notes
  • Network: The package makes network calls to external URLs, which is common for fetching random data but should be monitored for unexpected destinations.
  • Shell: No shell execution patterns detected, indicating low risk for direct system command execution.
  • Obfuscation: The use of eval() with dynamic command execution indicates potential code injection vulnerabilities and obfuscation techniques.
  • Credentials: No direct evidence of credential harvesting patterns was found in the provided snippets.
  • Metadata: The package shows signs of low maintainer effort and lack of transparency, which raises some concerns but does not conclusively indicate malicious intent.

📦 Package Quality Overall: Low (3.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
○ 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
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 250 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 7.5

Found 5 network call pattern(s)

  • [str, object]: response = requests.get(_RANDOM_USER_URL, params={"nat": locale[:2], "results": 1},
  • range(count): resp = requests.get(_RANDOM_NAME_URL, timeout=5) if resp.status_code ==
  • Client, None]: async with httpx.AsyncClient(base_url=UPSTREAM, timeout=10.0) as client: yield cl
  • lifecycle.""" async with httpx.AsyncClient(base_url=UPSTREAM, timeout=10.0) as client: yield cl
  • ttp_client _http_client = httpx.AsyncClient(timeout=10.0) _db_pool["products"] = { 1: {"id":
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • .join([self._stringify_result(eval(command, eval_scope)) for _ in range(sample.size)])
  • to pass a command string to `eval()` should the string pass the validation performed by th
  • s within the scope where `eval()` will be called, {method} will be the provider method
  • is also available within the `eval()` scope, and {arguments} will be sample arguments parse
  • ions.OrderedDict` within the `eval()` scope before passing the command string to `eval()` f
  • assing the command string to `eval()` for execution. This can be done in code review. """
✓ 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 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with argot-engine 
Create a command-line tool named 'ArgotAnalyzer' using the Python package 'argot-engine'. This tool will serve as a linguistic analysis assistant, designed to help users understand and analyze text based on its underlying cultural and social contexts. Here’s how you can approach building this tool:

1. **Project Setup**: Start by setting up your Python environment. Ensure you have Python 3.8 or later installed. Install 'argot-engine' via pip if it's not already installed.

2. **Core Functionality**: The primary function of ArgotAnalyzer will be to take input text and output a detailed analysis of any subcultures, slang, or specialized language used within the text. It should also provide suggestions on how to translate or interpret these terms in broader contexts.

3. **User Interface**: Since this is a command-line tool, focus on creating a user-friendly interface where users can easily input their text. Provide options to read from standard input or from a file.

4. **Features**:
   - **Text Analysis**: Automatically identify and categorize any argot (slang or specialized language) present in the text.
   - **Contextual Translation**: Offer translations or explanations of identified argots into more common language forms.
   - **Usage Examples**: Provide examples of how these argots might be used in different contexts.
   - **Custom Dictionary Support**: Allow users to add their own definitions or translations to the dictionary used by 'argot-engine', enhancing the tool's accuracy over time.

5. **Integration with 'argot-engine'**: Use 'argot-engine' to perform the core linguistic analysis tasks. Ensure that your tool leverages the package's capabilities to accurately detect and analyze argots in the input text.

6. **Testing and Documentation**: Write tests to ensure your tool works as expected across various types of input texts. Document the usage of your tool thoroughly, including how to install dependencies and run the tool with different options.

7. **Deployment**: Once your tool is complete, consider deploying it as a standalone executable or as a Docker container for easy sharing and distribution.

Your goal is to create a tool that not only performs well but is also accessible and useful for anyone interested in understanding the nuances of language and culture through text analysis.

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