agentboundary

v0.1.0 suspicious
4.0
Medium Risk

Open spec and conformance suite for proving AI-initiated production actions

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risks across network, shell, obfuscation, and credential fronts, but the metadata risk due to an incomplete and new maintainer profile adds concern.

  • Incomplete maintainer profile
  • New/inactive maintainer account
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected.
  • Shell: Shell execution patterns observed are typical for package installation and virtual environment management, suggesting benign use.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has an incomplete profile and a new/inactive account, which raises some suspicion but does not strongly indicate malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • letedProcess[str]: return subprocess.run( [sys.executable, "-m", "agentboundary", *args],
  • wheel_outdir.mkdir() subprocess.run( [sys.executable, "-m", "build", "--wheel", "--outdi
  • v_dir = tmp_path / "venv" subprocess.run([sys.executable, "-m", "venv", str(venv_dir)], check=True)
  • mat checkers are present) subprocess.run( [str(venv_python), "-m", "pip", "install", "--quiet
  • d prints the $id result = subprocess.run( [ str(venv_python), "-c",
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: jamjet.dev>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository jamjet-labs/agentboundary 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 agentboundary 
Create a mini-application that acts as a compliance checker for AI-driven workflows using the 'agentboundary' package. This application will serve as a proof-of-concept tool to demonstrate how AI systems can be monitored and ensured to comply with predefined operational boundaries and ethical guidelines. The application should include the following functionalities:

1. **User Interface**: Develop a simple, intuitive user interface that allows users to input their AI workflow details and select compliance checks.
2. **AI Workflow Input**: Users should be able to define their AI workflows, including key actions and decision points, through a form or configuration file upload.
3. **Compliance Checks**: Implement various compliance checks based on the 'agentboundary' package, such as verifying if the AI actions adhere to legal standards, ethical guidelines, or specific organizational policies.
4. **Results Display**: After running the compliance checks, display the results in a clear format, indicating which parts of the AI workflow meet the compliance criteria and which do not.
5. **Customizable Compliance Rules**: Allow users to customize compliance rules based on their specific needs, leveraging the flexibility provided by 'agentboundary'.
6. **Integration with External Systems**: Optionally, integrate the application with external systems like logging services or alerting tools to notify stakeholders about compliance issues in real-time.

Utilize the 'agentboundary' package to handle the core logic of compliance checking, ensuring that your application can dynamically adapt to different compliance requirements without needing extensive code changes. This project aims to showcase the practical application of 'agentboundary' in enhancing the transparency and accountability of AI systems.