any-guardrail

v0.5.1 suspicious
5.0
Medium Risk

(No description)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to potential network interactions and execution of shell commands, which could lead to unintended behavior or vulnerabilities if not properly managed.

  • network risk
  • shell risk
Per-check LLM notes
  • Network: Network calls may be legitimate if the package is designed to interact with external services.
  • Shell: Execution of shell commands can pose significant risks if not properly sanitized or controlled.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some signs of low maintainer activity and metadata quality but does not indicate clear malicious intent.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • Test runner config found: pyproject.toml
  • Test runner config found: conftest.py
  • 6 test file(s) detected (e.g. test_all.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://mozilla-ai.github.io/any-guardrail/
  • 1 documentation file(s) (e.g. test_all.py)
  • Detailed PyPI description (3683 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

  • 172 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 9 unique contributor(s) across 100 commits in mozilla-ai/any-guardrail
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • ry: request = urllib.request.Request( # noqa: S310 - URL targets the encoderfile subproc
  • ) with urllib.request.urlopen(request, timeout=2.0) as resp: # noqa: S310 - URL t
  • de("utf-8") request = urllib.request.Request( # noqa: S310 - URL targets the encoderfile subproc
  • POST", ) with urllib.request.urlopen(request, timeout=self.request_timeout) as resp: # n
  • ry: request = urllib.request.Request( # noqa: S310 - URL targets the llamafile subproces
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • e None self.process = subprocess.Popen(cmd, stdout=stdout, stderr=stdout) # noqa: S603 - cmd built
✓ 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

Repository mozilla-ai/any-guardrail appears legitimate

⚠ 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 any-guardrail 
Develop a Python-based mini-application called 'SafeSearch' that leverages the 'any-guardrail' package to ensure safe and ethical usage of AI models for generating search results. The application will take user queries as input and return filtered search results that adhere to strict safety guidelines. Here are the steps and features to include in your project:

1. **Setup**: Begin by installing the necessary packages including 'any-guardrail'. Ensure your environment is set up properly with all dependencies.
2. **Integration of 'any-guardrail'**: Utilize 'any-guardrail' to monitor and control the output of AI models used within the application. This includes setting up guardrails to prevent the generation of harmful content.
3. **User Input Handling**: Design a simple interface where users can enter their search queries. The application should be able to handle natural language inputs effectively.
4. **Query Processing**: Implement logic to process the user query using an AI model. Use 'any-guardrail' to evaluate and filter the results based on predefined safety criteria.
5. **Result Filtering**: Apply the guardrails provided by 'any-guardrail' to filter out any unsafe or inappropriate results before displaying them to the user.
6. **Feedback Mechanism**: Include a feature where users can report any results they find inappropriate. The feedback should be logged and used to further refine the guardrails.
7. **Documentation**: Write comprehensive documentation detailing how to use the application, install dependencies, and understand the workings of 'any-guardrail'.

The goal is to create a robust and user-friendly tool that ensures the ethical use of AI in generating search results, making it a valuable resource for educational and professional settings.

💬 Discussion Feed

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