atensec-thoth

v0.1.17 safe
3.0
Low Risk

AI agent governance SDK by Aten Security

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators, with no evidence of malicious intent or harmful behavior. The primary concern lies in its network communications, though this aligns with its described functionality.

  • Low risk scores across all categories except network.
  • Purposeful network communication is consistent with the package's stated functionality.
Per-check LLM notes
  • Network: The observed network calls suggest the package communicates with external servers, which is not inherently malicious but requires further investigation into its purpose.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
  • Metadata: The maintainer has only one package, which might indicate a new or less active account, but no other suspicious activities were detected.

📦 Package Quality Overall: Medium (5.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.atensecurity.com
  • Detailed PyPI description (25688 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 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 81 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 23 commits in atensecurity/thoth-py
  • Single author but highly active (23 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • en_value self._http = httpx.Client( headers=headers, timeout=_HTTP_TIME
  • rcer_url self._http = httpx.Client(base_url=enforcer_url, headers=headers, timeout=_TIMEOUT)
  • T) self._async_http = httpx.AsyncClient(base_url=enforcer_url, headers=headers, timeout=_TIMEOUT)
  • else {} self._http = httpx.Client(base_url=config.resolved_enforcer_url, timeout=_HTTP_TIMEOUT
  • s) self._async_http = httpx.AsyncClient(base_url=config.resolved_enforcer_url, timeout=_HTTP_TIMEOUT
✓ Code Obfuscation

No obfuscation patterns detected

✓ 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: atensecurity.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository atensecurity/thoth-py appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Aten Engineering" 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 atensec-thoth 
Create a mini-application called 'ThothGuard' that leverages the 'atensec-thoth' package to manage and govern AI agents within a secure environment. This application will serve as a sandbox where users can deploy, monitor, and control AI agents, ensuring they operate within predefined security policies. Here’s a detailed breakdown of the project requirements:

1. **User Authentication**: Implement a simple user authentication system using OAuth2 or JWT tokens to ensure only authorized users can interact with the AI agents.
2. **Agent Deployment**: Allow users to upload their AI agent code or models. ThothGuard should then securely deploy these agents into isolated environments based on the user’s permissions and the organization's security policies.
3. **Monitoring and Logging**: Provide real-time monitoring and logging capabilities to track the behavior of deployed AI agents. Users should be able to view logs, performance metrics, and any anomalies detected by the system.
4. **Policy Enforcement**: Utilize the 'atensec-thoth' package to enforce strict security policies over the AI agents. These policies could include data access restrictions, communication protocols, and operational boundaries.
5. **Compliance Checks**: Integrate a feature that periodically checks if all deployed AI agents comply with the latest security standards and regulations. Any non-compliance should trigger alerts and recommendations for remediation.
6. **User Interface**: Develop a clean and intuitive web interface using Flask or Django for managing the AI agents. The UI should allow users to easily navigate through different functionalities like deploying new agents, viewing logs, and checking compliance status.
7. **Documentation and Setup Guide**: Include comprehensive documentation and a setup guide to help other developers understand how to use ThothGuard and integrate it with existing systems.

The 'atensec-thoth' package will be central to implementing the policy enforcement and compliance checks functionalities. It provides tools and APIs to define, apply, and audit governance rules over AI agents, making sure they adhere to strict security guidelines. By utilizing this package, ThothGuard aims to set a new standard for secure AI deployment and management.

💬 Discussion Feed

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