attestix

v0.4.0 safe
4.0
Medium Risk

Attestix - Attestation Infrastructure for AI Agents. DID-based agent identity, W3C Verifiable Credentials, EU AI Act compliance, delegation chains, and reputation scoring. 47 MCP tools across 9 modules.

🤖 AI Analysis

Final verdict: SAFE

The package shows some signs of potential misuse, such as network requests and obfuscation of sensitive data, but these practices can be legitimate. There is no clear evidence of malicious intent.

  • moderate network risk
  • some obfuscation of sensitive data
Per-check LLM notes
  • Network: The use of httpx.Client suggests network requests which could be legitimate for fetching data or making API calls, but warrants further investigation into its purpose.
  • Shell: No shell execution patterns detected, indicating a low risk of direct system command execution.
  • Obfuscation: The use of base64 encoding for private keys suggests some level of obfuscation, but it is commonly used for handling sensitive information securely.
  • Credentials: No explicit patterns indicating credential harvesting were detected.
  • Metadata: The maintainer has a new or inactive account with limited package history and missing author information.

📦 Package Quality Overall: Medium (6.0/10)

◈ 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://attestix.io/docs
  • Detailed PyPI description (21258 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

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

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in VibeTensor/attestix
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • tname} try: with httpx.Client(timeout=timeout, follow_redirects=False) as client:
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • priv_bytes = base64.b64decode(data["private_key_b64"]) except Exception as e:
✓ 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: vibetensor.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository VibeTensor/attestix 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 attestix 
Create a mini-application named 'AI Agent Trust Network' using the Python package 'attestix'. This application will serve as a simplified version of an attestation infrastructure for AI agents, focusing on key aspects like DID-based agent identities, W3C Verifiable Credentials, EU AI Act compliance, delegation chains, and reputation scoring. Here's a step-by-step guide on how to develop the application:

1. **Setup**: Begin by setting up your development environment. Ensure you have Python installed along with the necessary packages, including 'attestix'. Initialize a new Python project and install 'attestix' via pip.
2. **Agent Registration**: Implement a feature where users can register their AI agents. Each agent will receive a unique DID (Decentralized Identifier) for identification purposes. Use 'attestix' to generate and manage these DIDs.
3. **Credential Issuance**: Develop functionality for issuing W3C Verifiable Credentials to agents upon registration or when they fulfill certain criteria. These credentials could include information about the agent's capabilities, compliance status, etc. Utilize 'attestix' to create, sign, and store these credentials securely.
4. **Compliance Check**: Incorporate a module that checks if the registered AI agents comply with the EU AI Act guidelines. This could involve verifying specific attributes within the agents' verifiable credentials. Leverage 'attestix' for parsing and validating these credentials against predefined rules.
5. **Delegation Chains**: Allow agents to delegate certain actions or permissions to other agents. Implement a system for creating and managing these delegation relationships using 'attestix', ensuring that all delegations are properly attested and verifiable.
6. **Reputation System**: Design a basic reputation scoring system for agents based on their interactions and compliance history. Scores could be adjusted positively or negatively based on feedback from other agents or users. Use 'attestix' to track and verify reputation changes through verifiable credentials.
7. **User Interface**: Create a simple web interface (using Flask or Django) that allows users to interact with the system. Users should be able to view agent details, issue credentials, check compliance, manage delegations, and see reputation scores. Integrate 'attestix' functionalities into the backend to handle all the complex operations.
8. **Testing and Documentation**: Thoroughly test the application to ensure all features work as expected. Write comprehensive documentation explaining how each part of the application works, especially how 'attestix' is utilized throughout.

By following these steps, you'll develop a functional mini-application that demonstrates the power and versatility of 'attestix' in building secure, compliant, and trustworthy AI agent infrastructures.

💬 Discussion Feed

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