aport-agent-guardrails

v1.0.29 suspicious
4.0
Medium Risk

APort Agent Guardrail — shared core for AI agent and LLM frameworks (pre-action authorization)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in network, shell, obfuscation, and credential areas. However, the metadata risk and shell risk raise some concerns, suggesting potential issues that require closer monitoring.

  • Maintainer has only one package
  • Subprocess execution detected
Per-check LLM notes
  • Network: No network calls detected, which is low risk.
  • Shell: Subprocess execution is detected but without clear malicious intent. Further review of the subprocess commands is needed.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 8 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/aporthq/aport-agent-guardrails/tree/main/
  • Brief PyPI description (427 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

  • 73 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in aporthq/aport-agent-guardrails
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • try: r = subprocess.run(cmd, check=False, timeout=120, env=env) return r
  • non-interactive") r = subprocess.run( cmd, check=False, timeo
  • text) try: proc = subprocess.run( [guardrail_script, tool_name, context_json],
✓ 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 aporthq/aport-agent-guardrails appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "APort Technologies Inc." 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 aport-agent-guardrails 
Create a Python-based mini-application named 'GuardRailAI' that leverages the 'aport-agent-guardrails' package to ensure safe and ethical operations of AI agents and LLMs. This application will serve as a robust guardrail system for developers looking to implement AI functionalities within their projects while maintaining control over the actions performed by these AI entities.

**Application Features:**
1. **User Interface:** Develop a simple yet effective user interface where users can input commands or queries intended for an AI agent or LLM.
2. **Pre-Action Authorization:** Utilize 'aport-agent-guardrails' to perform pre-action authorization checks on every command sent to the AI agent or LLM. This includes verifying if the action is within predefined ethical guidelines, checking against malicious intent, and ensuring data integrity.
3. **Customizable Rules Engine:** Allow users to define their own rules for what constitutes acceptable behavior from the AI agent or LLM. These rules could range from content moderation to operational constraints.
4. **Audit Trail:** Maintain a log of all actions taken by the AI agent or LLM, including any modifications made due to guardrail interventions. This audit trail should be easily accessible for review.
5. **Real-time Feedback:** Provide real-time feedback to users about why certain actions were blocked or modified based on the guardrail checks.
6. **Integration Capabilities:** Ensure the application can integrate with various AI agent or LLM frameworks, allowing for broad applicability across different development environments.

**Implementation Steps:**
1. Set up the project environment, installing necessary packages including 'aport-agent-guardrails'.
2. Design and implement the user interface, focusing on ease-of-use and clarity.
3. Integrate 'aport-agent-guardrails' into the application flow, ensuring it performs pre-action checks before any command is executed by the AI agent or LLM.
4. Develop the customizable rules engine, allowing for flexible definition of guardrail policies.
5. Implement logging and auditing functionality to track all interactions and interventions.
6. Test the application thoroughly with various scenarios to ensure reliability and effectiveness of the guardrail system.
7. Document the setup process, usage instructions, and best practices for integrating 'GuardRailAI' into existing projects.

By following these steps and utilizing the 'aport-agent-guardrails' package effectively, you'll create a valuable tool for developers aiming to harness the power of AI while safeguarding against potential misuse or unintended consequences.

💬 Discussion Feed

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