baar-core

v0.7.1 suspicious
6.0
Medium Risk

Budget-Aware Agentic Routing — route LLM calls intelligently between cheap and powerful models with a hard budget cap.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant shell risk due to subprocess execution without proper sanitization, which is a critical security flaw. Additionally, there is moderate network and obfuscation risk, though no direct evidence of malicious intent was found.

  • High shell risk due to potential for command execution
  • Moderate network risk with undocumented external API calls
  • Some level of code obfuscation without clear purpose
Per-check LLM notes
  • Network: The network call to an external API is unusual and could indicate data being sent outside the system without clear documentation or user consent.
  • Shell: Subprocess execution without proper sanitization or input validation is risky and could be indicative of potential code injection or command execution vulnerabilities.
  • Obfuscation: The observed pattern is likely an attempt to obfuscate code, but without further context, it's unclear if this is malicious or a legitimate use like dynamic importation.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 20 test file(s) found

  • Test runner config found: pyproject.toml
  • 20 test file(s) detected (e.g. test_alerts.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://baar.robatdasorvi.com
  • Detailed PyPI description (19040 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

  • 155 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 75 commits in orvi2014/Baar-Core
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • try: with socket.create_connection(("127.0.0.1", self._port), timeout=0.2):
  • : str) -> str: return requests.get(f"https://api.search.com?q={query}").text @baar_guard(r
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • __import__("baar.core.models", fromlist=["StepResult"]).StepResult( step_num=1,
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • " self._hermes_proc = subprocess.Popen( ["hermes"], env=self._hermes_env(),
  • " self._hermes_proc = subprocess.Popen( ["hermes"], stdin=subprocess.PIPE,
✓ 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 orvi2014/Baar-Core appears legitimate

⚠ 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 baar-core 
Create a mini-budget-aware AI chatbot using the 'baar-core' package. This application will allow users to interact with various AI models (both cheaper and more expensive ones) while ensuring that their total cost of interactions does not exceed a predefined budget. Here’s how you can build it step-by-step:

1. **Setup Project Environment**: Start by setting up a Python environment. Ensure you have the 'baar-core' package installed. If not, install it via pip.

2. **Define User Interface**: Develop a simple command-line interface or a basic web interface where users can input their queries and receive responses from the AI models.

3. **Integrate baar-core**: Use 'baar-core' to manage the routing of user queries to different AI models based on their budget-awareness criteria. This includes deciding which model to use for each query based on its cost and expected quality of response.

4. **Implement Budget Management**: Allow users to set a budget limit for their interactions with the AI models. Track the cost of each interaction and ensure that the total cost stays within the user-defined budget.

5. **Feature Suggestions**:
   - **Model Selection**: Provide users with options to choose between different models based on their preferences for cost vs. quality.
   - **Cost Estimation**: Before sending a query, provide an estimate of the cost based on the chosen model.
   - **Budget Alerts**: Notify users when they are approaching their budget limit.
   - **Usage Analytics**: Offer analytics on how much has been spent, average cost per interaction, etc.

6. **Testing and Deployment**: Thoroughly test your application to ensure it works as expected. Once ready, deploy it either as a standalone application or as a web service.

By following these steps, you'll create a practical and innovative tool that leverages the 'baar-core' package to make AI interactions more accessible and affordable.

💬 Discussion Feed

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