aisbf

v0.99.67 suspicious
5.0
Medium Risk

AISBF - AI Service Broker Framework || AI Should Be Free - A modular proxy server for managing multiple AI provider integrations

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential obfuscation and has metadata risks, but lacks clear indicators of credential harvesting or malicious intent.

  • base64 decoding without clear context
  • suspicious non-HTTPS link in metadata
Per-check LLM notes
  • Obfuscation: The presence of base64 decoding without clear context suggests potential obfuscation practices.
  • Credentials: No explicit patterns for harvesting credentials were detected.
  • Metadata: Suspicious non-HTTPS link and single-package maintainer indicate potential risk.

📦 Package Quality Overall: Low (3.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://git.nexlab.net/nexlab/aisbf.git
  • Detailed PyPI description (9170 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 570 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 4.5

Found 3 network call pattern(s)

  • n API call async with httpx.AsyncClient() as client: response = await client.post(
  • ) async with httpx.AsyncClient() as client: response = await client.post(
  • " async with httpx.AsyncClient() as client: response = await client.post(
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • str): return base64.b64decode(chunk["data_base64"]) if chunk.get("encoding") =
  • str): return base64.b64decode(chunk["data"]) if "chunk" in chunk:
  • e64"): return base64.b64decode(envelope["body_base64"]), content_type if isinst
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: nexlab.net

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://aisbfity4ud6nsht53tsh2iauaur2e4dah2gplcprnikyjpkg72vfjad.onion
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "AISBF Contributors" 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 aisbf 
Create a versatile mini-app named 'AI-ProxyManager' using the Python package 'aisbf'. This app will serve as a bridge between users and various AI services, making it easier to switch between different AI providers without changing the underlying code. Here’s a step-by-step guide on how to develop this application:

1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have Python installed, then install the 'aisbf' package via pip.
2. **Design Architecture**: Design a modular architecture where each AI service is represented as a plugin. Use 'aisbf' to manage these plugins dynamically.
3. **Core Features**:
   - **Service Discovery**: Implement a feature that allows the app to discover available AI services from the 'aisbf' framework.
   - **Dynamic Switching**: Enable users to switch between AI services seamlessly. For instance, if a user wants to try out a new AI service, they should be able to do so without restarting the application.
   - **Load Balancing**: Incorporate load balancing to distribute requests across multiple instances of the same AI service, enhancing performance and reliability.
4. **User Interface**: Develop a simple command-line interface (CLI) for interacting with the app. Users should be able to list available services, select a service, and send queries.
5. **Error Handling & Logging**: Ensure robust error handling and logging mechanisms are in place. Logs should capture all interactions with AI services, including errors and successful responses.
6. **Security Considerations**: Since this app will interact with various AI services, ensure data security. Implement basic security measures such as authentication tokens for accessing AI services.
7. **Testing & Documentation**: Write comprehensive tests to validate the functionality of each module. Also, create detailed documentation explaining how to use the app, including setup instructions and examples.

Throughout the development process, leverage the 'aisbf' package's capabilities to streamline integration with different AI services, manage configurations, and handle service-specific requirements.

💬 Discussion Feed

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